Date | Title | Provider |
2025 |
Agricultureal Security Areas of Adams County, Pennsylvania
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| Adams County |
2025 |
Land Conservancy Easements of Adams County, Pennsylvania
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| Adams County |
2025 |
Mt Joy Twp Preserved Farms of Adams County, Pennsylvania
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| Adams County |
2025 |
Adams County Municipal Boundaies
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| Adams County |
2025 |
Adams County parcels
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| Adams County |
2025 |
Preserved Farms of Adams County, Pennsylvania
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| Adams County |
2025 |
Adams County streets
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| Adams County |
2025 |
This dataset contains Address Points in Allegheny County. The Address Points were created by GDR for the Allegheny County CAD project, October 2008. Data is updated by County staff as changes and corrections are found, on a continuous basis. Updates are sent to PASDA monthly.
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| Allegheny County |
2002 |
3 Rivers Wet Weather Demonstration Program Outreach Planning Basins
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| Allegheny County |
2002 |
Derived from original MAPINDX: Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5" grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.
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| Allegheny County |
2016 |
This dataset contains the Allegheny County boundary.
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| Allegheny County |
2025 |
Footprints for all buildings and out buildings in Allegheny County. Buildings are captured by following the edge of the roof line. All near orthogonal corners are square. Interim roof lines, such as dormers, are not shown. Minor structures such as carports, decks, patios, stairs, etc. which are part of the structure are not shown. Building Footprints were updated as a result of a flyover in 2004. Buildings less than 400 square feet were not captured. Special consideration was given to garages, less than 400 square feet, and were digitized if greater than 200 square feet.
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| Allegheny County |
2002 |
Outlines of public and private cemeteries greater than one acre in size. Areas were delineated following a generalized line along the outside edge of the area. Individual features within the cemetery are not shown.
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| Allegheny County |
2006 |
Contour data was created as a result of a county flyover in the spring of 2004. Contours were created from a DTM Feature Dataset, upgraded by Baker and T-3 to support National Mapping Accuracy Standards (NMAS) for contours . Intermediate Contours are contours spaced at 5 foot intervals. Depression Contours are contours showing the edges and slope in a depression at 5 foot intervals. Contours are coded separately for delineation between depression and intermediate contours.
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| Allegheny County |
2008 |
Data was created to portray the boundaries of the Councils of Government (COG's) in Allegheny County.
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| Allegheny County |
2012 |
Data was created to portray the boundaries of the County Council Districts in Allegheny County.
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| Allegheny County |
2017 |
This dataset contains Farmers Market locations in Allegheny County.
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| Allegheny County |
2000 |
In an effort to expedite the permit review process for Water Obstruction and Encroachment Applications, the Pennsylvania Department of Environmental Protection initiated a plan to replace hard-copy maps with digital GIS sets. The project is referred to as the 105 Spatial Data System (105SDS) Pennsylvania river floodplains and coastal floodplains are two of many spatial data sets that were used in the 105SDS project. As a result of work completed by Law Environmental, Inc. on the statewide low-level radioactive waste siting project, DEP received two coverages depicting river and coastal floodplains. However, due to the process used in constructing these data sets, there were many areas throughout the state in which floodplains were not digitized. The primary purpose of this task was to complete the digital floodplain mapping in these areas.
Purpose: INTENDED USE OF DATA; Created to do permit reviews for Water Obstruction and Encroachment Applications. LIMITATIONS OF DATA; Due to the nature of transferring the floodplains from the Federal Emergency Management Agency maps to plotted 1:24000 scale maps this coverage should be considered to be the "best representation" of the data but not as accurate as, for example, a map of Global Positioning System's floodplain coordinates
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| Allegheny County |
2010 |
The Greenways feature class consists of a compilation of the following data: agricultural easements, Allegheny Land Trust GREENPRINT, Conservation Streams buffered by 50 ft, Forested Floodplains, City of Pittsburgh designated Greenways, Land Trust Properties, Rivers buffered by 100 ft, sensitive slopes, wetlands 1 acre or more buffered by 50 ft, golf courses, parks and trails. Building footprints have been deleted from the feature class, but can be added back (see ModelWorkspace\Buildings_in_Greenways).
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| Allegheny County |
2016 |
Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Reservoirs are man made embankments of water. Included in this definition are both covered and uncovered water tanks.
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| Allegheny County |
2016 |
Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Reservoirs are man made embankments of water. Included in this definition are both covered and uncovered water tanks.
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| Allegheny County |
2000 |
Derived from original MAPINDX: Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5" grid tics and italicized grid coordinate markers and outlines of map sheet boundaries. Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.
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| Allegheny County |
2002 |
Data was created to portray the Libraries in Allegheny County.
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| Allegheny County |
2024 |
Data was created to portray the boundaries of the 130 Municipalities in Allegheny County the attribute table includes additional descriptive informations including Council of Government (COG) affiliation, School District, Congressional District, FIPS Code, County Municipal Code and County Council District.
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| Allegheny County |
2000 |
The Allegheny County Natural Heritage Inventory identifies and maps Allegheny County's most significant natural areas. The NHI study (Natural Heritage Inventory) investigated plant and animal species and communities that are unique or uncommon in Allegheny County; it also explored areas important for general wildlife habitat, education and scientific study.
The inventory does not confer protection on any of the areas listed here.
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| Allegheny County |
2000 |
NWI (National Wetlands Inventory) digital data files are records of wetlands location and classification as defined by the U.S. Fish & Wildlife Service. This dataset is one of a series available in 7.5 minute by 7.5 minute blocks containing ground planimetric coordinates of wetlands point, line, and area features and wetlands attributes. When completed, the series will provide coverage for all of the contiguous United States, Hawaii, Alaska, and U.S. protectorates in the Pacific and Caribbean. The digital data as well as the hardcopy maps that were used as the source for the digital data are produced and distributed by the U.S. Fish & Wildlife Service's National Wetlands Inventory project. This data set is derived from the national coverage and only includes the area within the boundary of Allegheny County.
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| Allegheny County |
2025 |
Boundary outlines of individual properties in Allegheny County.
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| Allegheny County |
2000 |
Paved and unpaved parking lots that accommodate more than ten (10) cars.
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| Allegheny County |
2024 |
Was derived from the data included in original file CULTUREF. Delineates the area following a generalized line along the outside edge of all parks in Allegheny County
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| Allegheny County |
2000 |
Data was created to portray in-ground community pools, does not include residential pools.
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| Allegheny County |
2002 |
Point locations of county and municipal buildings, halls, public works sites, government buildings.
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| Allegheny County |
2006 |
Railroad Centerlines are collected by digitizing the apparent center of each set of rails. The Rail Line Edge is the apparent Right of Way. Rights of Way and Centerlines that are currently being used for rail traffic are coded as In Use. Rights of Way and Centerlines that no longer maintain rail traffic but have tracks remaining, as exemplified by plants growing through or around the tracks or right of way, are coded as Abandoned. Rights of Way and Centerlines that have the tracks removed are coded as Old. The Rights of Way and Centerlines that have the tracks removed and have been converted to trails are coded as Rails to Trail. For rail lines that cannot be discerned between Old and Rails to Trails, the Rights of Way and Centerlines shall be coded as Old.
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| Allegheny County |
2006 |
The Retaining Wall Feature Dataset contains photogrammetrically compiled Retaining Walls - structures of concrete, brick, stone, wood, etc. retaining earth and adjacent to a road, railroad, edge or stream that are over five (5) feet high and 200 feet long.
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| Allegheny County |
2010 |
Slope data
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| Allegheny County |
2000 |
Data was created to portray the soils in Allegheny County.
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| Allegheny County |
2006 |
Spot Elevation data was created as a result of a county flyover in the spring of 2004. They were created from a DTM Feature Dataset, upgraded by Baker and T-3 to support National Mapping Accuracy Standards (NMAS). Spot Elevations are used to show additional elevation information. They are located in flat areas where contours may be sparse or spaced far apart, at road and railroad intersections, on the road centerline at the ends of bridges, on the road centerline over the center of culverts that have a span of five (5)-foot or greater, at the crest of all tops of hills, at saddles, within depressions and where the ground is visible in obscured areas.
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| Allegheny County |
2025 |
This dataset contains street centerlines for vehicular and foot traffic in Allegheny County. Street Centerlines are classified as Primary Road, Secondary Road, Unpaved Road, Limited Access Road, Connecting Road, Jeep Trail, Walkway, Stairway, Alleyway and Unknown.
A Primary Road is a street paved with either concrete or asphalt that has two (2) or more lanes in each direction. A Secondary Road is a residential type hard surface road, or any hard surface road with only one (1) lane in each direction. An Unpaved Road is any road covered with packed dirt or gravel. A Limited Access Road is one that can only be accessed from a Connecting Road such as an Interstate Highway. A Connecting Road is a ramp connecting a Limited Access Road to a surface street. A Walkway is a paved or unpaved foot track that connects two (2) roads together. Walkways within College Campuses will also be shown. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. Walkways will not have an Edge of Pavement feature. A Stairway is a paved or wooden structure that connects two (2) roads together. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. An Alleyway is a road, usually narrower than a Secondary Road that runs between, but parallel to, two (2) Secondary Roads. Generally, Outbuildings will be adjacent to Alleyways. A Jeep Trail is a vehicular trail used for recreation. A Jeep Trail will not have an associated edge of pavement feature. A road coded as Unknown is a road, which in the judgment of the photogrammetrist, does not fall into any of the categories listed.
Centerlines will be visually placed between the edges of pavement. One (1) centerline will be placed between each edge of pavement. Roads with medial strips, such as Limited Access Roads, will have two (2) centerlines for those portions of the road where the medial strip is present.
For roads that terminate with a cul-de-sac, the centerline shall continue through the center of the cul-de-sac and stop at the edge of pavement.
All attribute data will remain for all Street Centerlines that are not updated. For Street Centerlines that are new, the only attribute field that will be populated is the FeatureCode and UPDATE_YEAR. If a Street Centerline is graphically modified, the existing attribute data will remain and the UPDATE_YEAR will be set to 2004. The attribute values for 2004 Street Centerlines should be considered suspicious until verified.
The ArcInfo Street Centerline coverage that is being updated has 800 segments of Paper Streets, 66 segments of Vacated Streets and 78 segments of Steps. Street Centerlines that are coded as Paper Streets in the OWNER field will remain unchanged in the updated dataset unless the area has been developed. In the event the area has been developed, the Street Centerlines will be modified to reflect the true condition of the visible roads.
Street Centerlines that are coded as Vacated in the OWNER field will also remain unchanged in the updated dataset. In the event the area coinciding with the Vacated Streets has been developed, the Vacated Street Centerlines will be removed in order to reflect the true condition of the area.
Street Centerlines that are coded as Steps in the OWNER field will be updated to reflect the current condition of the area.
The Street Centerlines dataset consists of an external table that links to the supplied coverages and the Geodatabase created for this project using the "-ID" (UserID) field. In order to maintain the link to the external table and not loose valuable data the decision was made to keep all database information currently in the Street Centerline dataset. When a Street Centerline is modified during the update process, the field "UPDATE_YEAR" is set to 2004. All other database attributes will remain unchanged from the original values. All Street Centerline database data with an "UPDATE_YEAR" of 2004 should be verified before used. In some occasions the Street Centerline was divided into two (2) sections to allow for a new road intersection. Both sections of the resulting Street Centerline will have the same database attributes including Address Range. All new Street Centerlines will have zero (0) for "SystemID" and "UserID".
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| Allegheny County |
2016 |
Edge of Pavements are the edges of all public thoroughfares including paved roads, unpaved roads, bridges, overpasses, tunnels, alleys, stairways and airfield runways.
Paved Roads are roads that are surfaced with concrete or asphalt. Roads that have been tarred and chipped are also considered paved. Roads surfaced with gravel or packed dirt are considered Unpaved. An Alleyway is a road, usually narrower than surrounding roads that runs between, but parallel to, two paved roads. An Alley can be either paved or unpaved. Generally, Outbuildings will be adjacent to Alleyways. Road pavement edges that are underneath bridges or overpasses are coded as Hidden. Bridges and Overpasses are delineated along the outside edge of the structure. Portions of bridges that are under other bridges or other objects are coded as Hidden Bridge. Tunnels are shown as lines connecting the apparent width of the tunnel through the earth. Stairways are major pedestrian thoroughfares, either paved or wooden, connecting two (2) roads and typically found on steep hills. Airfield Runways are the edges of Runways, Taxiways and other airfield pavement areas. Driveways that are greater than 300' in length will be digitized and coded as either Paved Road or Unpaved Road.
Paved Road edges show the width of the paved area. If the paved road has a paved shoulder, the paved shoulder is shown as part of the paved road. The placement of an unpaved road is subject to interpretation by the photogrammetrist since the road edge may be indefinite due to the imprecise nature of an unpaved surface.
Driveways that are over 300' in length are digitized. For a driveway that terminates at a building, it shall be drawn to the building. Driveways will also have a centerline for its entire length.
Parking lots are not digitized. The entrance to a parking lot for shopping areas shall be shown. Driving areas around and through parking lots serving shopping areas and other businesses are considered part of the parking lot. If the driving area through a parking lot connects to two (2) or more exterior roads, the main thoroughfare through the parking lot shall be shown as a road.
The driving area through an apartment complex or condominium complex shall be digitized as a road. The road is needed in these areas to show access to the individual dwelling places.
Edges of Pavement that are updated will have the database field StreetCL_FC populated with the Feature Code of the Street Centerline. (The Street Centerline Feature Code is more descriptive than the general code used for Edge of Pavement.)
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| Allegheny County |
2010 |
Allegheny County Urban Tree Canopy. High resolution land cover dataset for Allegheny County, Pennsylvania.
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| Allegheny County |
2024 |
Data was created to portray the boundaries of the Voting Districts in Allegheny County.
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| Allegheny County |
2000 |
Polygons marking borders of watersheds in Allegheny county.
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| Allegheny County |
2011 |
Stands of trees (coniferous and deciduous) too numerous to plot as individual trees. The area is delineated following a generalized line along the outside edge of tree trunks. Areas are captured if at least one acre in size or of major significance especially in urban areas.
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| Allegheny County |
2024 |
This dataset demarcates the zip code boundaries that lie within Allegheny County. These are not clipped to the Allgeheny County boundary.
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| Allegheny County |
2023 |
Boundary outlines of individual properties in Bedford County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Bedford County |
2025 |
These address points represent current, and believed active physical 911 addresses. Sub-address data is incomplete and generally not spatially accurate to location within the parent structure/address. This is a constantly evolving dataset that sees both additions and deletions, as well as spatial adjustments on a regular basis. This dataset is a smaller subset of what is used by 911 dispatch, and thus we encourage any observed errors to be reported immediately to the Blair County GIS Department at GIS@blairco.org, or by calling the office at 814-693-2535.
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| Blair County |
2025 |
This dataset is widely used. It supports functions such as: 911 addressing, location reference, cartography, 911 dispatch, etc... Data is maintained by BCGIS Department. Because this data is a critical component to 911 addressing, if any errors are observed we kindly ask that the Blair County GIS Department be notified directly by sending an email to: GIS@blairco.org, or by calling the office at: 814-693-2535. Every effort is made to provide an accurate and current representation of the roads within the County, however we do not guarantee the validity nor spatial accuracy of this dataset.
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| Blair County |
2025 |
These parcels represent the current mapped parcels, to represent tax areas, not to serve as surveyed boundaries. Parcels polygons should never be considered survey accurate, nor should they be used in place of a survey by a registered land surveyor for any means other than general reference to the taxable area described on a deed. For any noticeable mapping errors please notify the Blair County Assessment Office at: blairassessment@blairco.org or by calling the office at: (814) 693-3110.
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| Blair County |
2023 |
Bradford County Access Lines
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| Bradford County |
2023 |
Bradford County Addresses
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| Bradford County |
2023 |
Bradford County Municipal Boundaries
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| Bradford County |
2023 |
Bradford County Parcel Hooks
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| Bradford County |
2023 |
Bradford County Parcels
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| Bradford County |
2023 |
Bradford County Road Centerlines
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| Bradford County |
2023 |
Agricultural Preservation Program
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| Bucks County |
2022 |
Drop Box Locations
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| Bucks County |
2023 |
Fire Stations
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| Bucks County |
2023 |
Land Trust Owned
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| Bucks County |
2020 |
Magisterial Districts
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| Bucks County |
2020 |
Major Roads
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| Bucks County |
2023 |
Municipal Boundary
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| Bucks County |
2023 |
Municipal Parks Open Space
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| Bucks County |
2023 |
Municipal Zoning
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| Bucks County |
2023 |
Natural Areas Program
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| Bucks County |
2020 |
PA Congressional Districts
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| Bucks County |
2020 |
PA State Assembly Districts
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| Bucks County |
2020 |
PA State Senate Districts
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| Bucks County |
2024 |
Parcels
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| Bucks County |
2023 |
Police Departments
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| Bucks County |
2022 |
Bucks County Polling Places
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| Bucks County |
2023 |
Proposed Developments
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| Bucks County |
2023 |
Road Centerlines
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| Bucks County |
2020 |
School Districts
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| Bucks County |
2023 |
Schools
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| Bucks County |
2024 |
Site Address Points
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| Bucks County |
2023 |
Trails
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| Bucks County |
2022 |
Bucks County Voting Precincts
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| Bucks County |
2022 |
Watersheds
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| Bucks County |
2023 |
Butler County, Pennsylvania address points.
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| Butler County |
2022 |
Municipalities boundary outlines of Butler County.
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| Butler County |
2023 |
Boundary outlines of individual properties in Butler County.
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| Butler County |
2023 |
Street Centerlines
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| Butler County |
2025 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2023 |
This dataset contains airport runways for Cambria County.
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| Cambria County |
2023 |
This dataset contains bridges of for Cambria County.
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| Cambria County |
2023 |
This dataset contains ditches for Cambria County.
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| Cambria County |
2023 |
This dataset contains driveways for Cambria County.
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| Cambria County |
2023 |
This dataset contains islands for Cambria County.
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| Cambria County |
2025 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2023 |
This dataset contains railroads for Cambria County.
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| Cambria County |
2023 |
This dataset contains rivers of Cambria County.
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| Cambria County |
2023 |
This dataset contains streams of Cambria County.
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| Cambria County |
2025 |
This dataset contains street centerlines for Cambria County.
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| Cambria County |
2023 |
This dataset contains structures of Cambria County.
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| Cambria County |
2023 |
This dataset contains waterbodies in Cambria County.
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| Cambria County |
2023 |
This dataset contains wetlands in Cambria County.
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| Cambria County |
2024 |
This dataset contains street centerlines for vehicular and foot traffic in Carbon County, Pennsylvania
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| Carbon County |
2024 |
Data was created to portray the boundaries of the 23 Municipalities in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Railroad Centerlines in Carbon County, Pennsylvania
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| Carbon County |
2020 |
School Districts in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Soils types in Carbon County, Pennsylvania
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| Carbon County |
2020 |
Polylgon geometric features representing the waterbodiies of Carbon County, Pennsylvania
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| Carbon County |
2020 |
Geometric features representing the waterways Carbon County, Pennsylvania
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| Carbon County |
2020 |
Zip code boundaries that lie within Carbon County
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| Carbon County |
2020 |
Boundaries of Carbon County Zoning Base Districts
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| Carbon County |
2023 |
Affordable Housing
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| Centre County |
2023 |
Electric Company Boundaries
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| Centre County |
2024 |
This Parcels layer contains basic geometry and attribute information. It is maintained by the Tax Assessment staff with the assistance of the GIS Department. This contains NO ownership information. Please contact the GIS Department for this dataset.
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| Centre County |
2023 |
Source Water Protection
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| Centre County |
2023 |
Streets
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| Centre County |
2023 |
Voting Locations
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| Centre County |
2023 |
Water Lines
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| Centre County |
2023 |
Water Treatment Plants
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| Centre County |
2023 |
Tunnels
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| Centre County |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2022 |
The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends. LC change between discrete time periods, detected by spectral changes in NAIP imagery and LiDAR, represents changes between the 12 land cover classes. LULC change uses LC change to identify where changes are happening and then LC is translated to LULC to represent transitions between the 54 LULC classes. The LULCC data is represented as a LULC class change transition matrix which provides users acres of change between multiple classes. It is organized by 18x18 and 54x54 LULC classes. The Chesapeake Bay Water (CBW) indicates raster tabulations were performed for only areas that fall inside the CBW boundary e.g., if user is interested in CBW portion of a county then they will use LULC Matrix CBW. Conversely, if they are interested change transitions across the entire county, they will use LULC Matrix. The database includes the following data: 1. 2013/2014 Land Cover (LC) 2. 2017/2018 Land Cover (LC) 3. 2013/2014 to 2017/2018 Land Cover Change (LCC) 4. 2013/2014 Land Use and Land Cover (LULC) 5. 2017/2018 Land Use and Land Cover (LULC) 6. 2013/2014 to 2017/2018 Land Use and Land Cover Change (LULCC) and LULCC matrices To start using the data please refer to the data_dictionary_2022-Edition.pdf (see under Attached Files). How to cite: When using the Chesapeake Bay Land Use/Land Cover Database or producing derivatives, the data must be properly cited based on the following criteria. Citing Entire Data Release Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L. Citing Land Cover (LC) and/or Land Cover Change (LCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Cover: U.S. Geological Survey data release. Developed by the University of Vermont Spatial Analysis Lab, Chesapeake Conservancy, and U.S. Geological Survey, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover (LULC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover: U.S. Geological Survey data release. Developed by the Chesapeake Conservancy, U.S. Geological Survey and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Land Use/Land Cover Change (LULCC) Products Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition: Land Use/Land Cover Change: U.S. Geological Survey data release. Developed by the U.S. Geological Survey, Chesapeake Conservancy, and University of Vermont Spatial Analysis Lab, https://doi.org/10.5066/P981GV1L. Citing Data Dictionary Chesapeake Bay Program, 2023, Chesapeake Bay Land Use and Land Cover Database 2022 Edition – Data Dictionary for the Chesapeake Bay Land Use/Land Cover Database, 2022 Edition: U.S. Geological Survey data release, https://doi.org/10.5066/P981GV1L
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| Chesapeake Bay Program |
2023 |
This dataset contains Address Points in Chester County
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| Chester County |
2011 |
One of the planimetric layers developed and owned by Chester County as part of the Chester County Land Records System (ChesCO-LRS) project. The airports were photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993.
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| Chester County |
2013 |
One of the planimetric layers developed and owned by Chester
County as part of the Chester County Land Records System
(ChesCO-LRS) project. The bridge data was photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993 and other sources.
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| Chester County |
2015 |
Footprints for all buildings and out buildings in Chester County.
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| Chester County |
2013 |
One of the planimetric layers developed and owned by Chester
County as part of the Chester County Land Records System
(ChesCO-LRS) project. The cemetery data was photogrammetrically compiled (stereo digitized) from the aerial photography taken April 07, 1993.
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| Chester County |
2011 |
The data describes the boundary of the Chester County. The boundary was captured from the County tax assessment maps. Original maps at 1"=400' and 1"=100' scales were compiled at 1"=200' through the digital tax parcel conversion project.
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| Chester County |
2020 |
Polling place locations and information within Crawford County PA
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| Chester County |
2013 |
Higher education facilities in Chester County. The attributing
contains education facility name.
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| Chester County |
2023 |
The data describes the municipal boundaries within Chester County. There are 73 municipalities within Chester County. The boundaries were captured from the County tax assessment maps. Original maps at 1"=400' and 1"=100' scales were compiled at 1"=200' through the digital tax parcel conversion project.
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| Chester County |
2023 |
Boundary outlines of individual properties in Chester County
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| Chester County |
2020 |
This dataset contains road centerlines for vehicular and foot traffic in Chester County.
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| Chester County |
2013 |
School Districts in Chester County.
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| Chester County |
2013 |
Schools in Chester County with their names in the attribute files.
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| Chester County |
2017 |
Zipcodes of Chester County
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| Chester County |
2025 |
The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983
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| City of Philadelphia |
2016 |
PhillyRising’s 19 active neighborhoods as of FY15. These areas were identified in partnership with the Police Department for their high rates of violent crime and quality of life issues, including poverty. PhillyRising strives to advance City services and community partnerships within these neighborhoods.
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| City of Philadelphia |
2016 |
The Choice Neighborhoods program is administered by the U.S. Department of Housing and Urban Development (HUD). It supports locally driven strategies to address struggling neighborhoods with distressed public or HUD-assisted housing through a comprehensive approach to neighborhood transformation.
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| City of Philadelphia |
2025 |
boat launches along the two major bodies of water, the Schuylkill River and the Delaware River. These boat launched have direct access into either one of these bodies of water.
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| City of Philadelphia |
2025 |
The points of pools owned and managed by Philadelphia Parks and Recreation. This layer was created to be used for the Philadelphia Parks and Recreation website.
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| City of Philadelphia |
2025 |
The points of spraygounds owned and managed by Philadelphia Parks and Recreation. This layer was created to be used for the Philadelphia Parks and Recreation website.
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| City of Philadelphia |
2025 |
Displays the locations of adult exercise equipment located within or are maintained by Philadelphia Parks and Recreation (PPR).
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| City of Philadelphia |
2025 |
Footprints of buildings and structures located on Philadelphia Parks and Recreation (PPR) properties or utilized directly by PPR.
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| City of Philadelphia |
2025 |
Point representations of Philadelphia Parks and Recreation (PPR) help locators. These are virtual points representing signage locations and their vehicular access points.
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| City of Philadelphia |
2025 |
Displays the locations of playgrounds within PPR Boundaries. Playgrounds designated as similar age range equipment within a definable distance (not each piece of equipment).
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| City of Philadelphia |
2025 |
Point location feature for all PPR Program Sites. This includes all recreation centers, playgrounds, older adult centers, swimming pools, and environmental education centers.
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| City of Philadelphia |
2025 |
Location and boundaries of lands that Philadelphia Parks and Recreation is responsible or has a distinct role in maintaining or managing.
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| City of Philadelphia |
2025 |
Displays the locations of outdoor tennis courts located within Philadelphia Parks and Recreation boundaries.
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| City of Philadelphia |
2023 |
Locations of trees that Philadelphia Parks and Recreation (PPR) inventories within the limits of the City of Philadelphia. This dataset is a snapshot in time.
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| City of Philadelphia |
2004 |
Planimetric Coverage containing the delineation of topographic contours at ten foot intervals. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200'. The city-wide contour download file is approximately 250 megabytes.
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| City of Philadelphia |
2004 |
Planimetric Coverage containing the delineation of topographic contours at two foot intervals. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200'. The city-wide contour download file is approximately 250 megabytes.
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| City of Philadelphia |
2008 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2008. DEMs were generated from the raw data.
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| City of Philadelphia |
2010 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2010. DEMs were generated from the raw data.
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 10ft Contours for the 2015 1ft Contours.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. Lidar Data Products for the Philadelphia, PA collection area including a 5ft Digital Elevation Model (DEM), and tiled1ft Contours.
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| City of Philadelphia |
2015 |
The lidar dataset was collected to be utilized for the creation of a digital elevation model and 1ft contours. Other uses expected. The GIS Services Group at OIT generated these 2ft Contours from the 2015 1ft Contours.
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| City of Philadelphia |
2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
DEM/Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEM/Hillshade was generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
Planimetric Coverage containing the delineation of topographic contours at ten foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels
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| City of Philadelphia |
2018 |
Planimetric Coverage containing the delineation of topographic contours at two foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2022 |
Contours 10ft - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
DEM - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
Hillshade - LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2022 |
Topographic Contours 1ft - Planimetric Coverage containing the delineation of topographic contours at one foot intervals. Annotation of Contour Line elevations exists. LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
1996 |
Philadelphia aerial photography
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| City of Philadelphia |
1996 |
Philadelphia aerial photography
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| City of Philadelphia |
2000 |
Philadelphia aerial photography
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| City of Philadelphia |
2000 |
Philadelphia aerial photography
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| City of Philadelphia |
2004 |
Philadelphia aerial photography
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| City of Philadelphia |
2004 |
Philadelphia aerial photography
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| City of Philadelphia |
2005 |
Philadelphia aerial photography
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| City of Philadelphia |
2005 |
Philadelphia aerial photography
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| City of Philadelphia |
2008 |
Philadelphia aerial photography
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| City of Philadelphia |
2008 |
Philadelphia aerial photography
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| City of Philadelphia |
2009 |
Philadelphia aerial photography
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| City of Philadelphia |
2009 |
Philadelphia aerial photography
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| City of Philadelphia |
2010 |
Philadelphia aerial photography
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| City of Philadelphia |
2010 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography - Leaf On
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2011 |
Philadelphia aerial photography
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| City of Philadelphia |
2012 |
Philadelphia aerial photography
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| City of Philadelphia |
2012 |
Philadelphia aerial photography
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| City of Philadelphia |
2014 |
Philadelphia aerial photography
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| City of Philadelphia |
2014 |
Philadelphia aerial photography
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| City of Philadelphia |
2015 |
Philadelphia aerial photography
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| City of Philadelphia |
2015 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography
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| City of Philadelphia |
2016 |
Philadelphia aerial photography 3 inch pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography city wide mosaic. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 1 meter pixels. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 1 meter pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 3 inch pixels. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2017 |
Philadelphia aerial photography 3 inch pixels tile index. The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International.
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| City of Philadelphia |
2018 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2018 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2019 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2019 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2020 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2020 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2022 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2022 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2023 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2023 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2024 |
Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2024 |
Tile Index - Color Citywide digital orthophotography with a ground resolution of at various resolutions, georeferenced to the Pennsylvania State Plane Coordinate System, and delivered as individual and mosaicked raster images. The dataset consists of tiled orthogonal imagery produced from nadir images captured by various contractors during the months of April and May.
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| City of Philadelphia |
2012 |
Philadelphia bike network and supporting datasets is an experimental dataset that combines various City of Philadelphia departments' datasets to support bike network routing. These datasets are in an experimental stage and are not yet approved for wide use. Use with caution is recommended. Datasets include Bike Network, Connector Streets, Regional Routes, and Trails and Side paths. For source, date, limitations and additional data for each supporting dataset, please see metadata.
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| City of Philadelphia |
2012 |
This dataset is a combination of both bike racks installed in 2008 as a result of Adopt-a-rack program and racks installed by converting parking meters by the Philadelphia Parking Authority.
Those racks installed in 2008 as part of the City's first Adopt-a-rack program were requested by civic and business associations who "adopted" them and promised to clear abandoned bikes, remove graffiti and maintain them if they were damaged. These bike racks are in the style of 'staple' or 'hoop' racks.
The PPA racks are those mounted to the former parking meter posts. They are the green discs that have been installed through Center City and parts of University City.
The PROJECT field defines the associated project of installation, either Adopt-a-rack or PPA.
The STAND_ADD field is most appropriate for address locating or geocoding purposes.
Please note the field NUM_RACKS represents the number of racks at a given address. Each record may represent more than one bike rack.
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| City of Philadelphia |
2010 |
Files used to comprise a 3D building model of the City of Philadelphia
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| City of Philadelphia |
2015 |
Files used to comprise a 3D building model of the City of Philadelphia
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| City of Philadelphia |
2017 |
Planimetric Coverage containing the delineation of buildings or related structure outlines that represent the footprints of buildings within the City of Philadelphia.
Outlines are based on imagery captured in early 2015.
FEATURES DELINEATED:
-Residential, commercial and industrial buildings
-Isolated garages, mobile homes, sheds
-Greenhouses and silos
-Buildings under construction that at least have walls
-Trailer boxes with windows or doors
Data was captured from AccuPLUS Orthomosaic tiles created off PAPHIL15-LEAF-OFF imagery, flown by Pictometry.
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| City of Philadelphia |
2010 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2025 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2025 |
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas. Some boundary details clarified by Philadelphia City Planning Commission May 2011
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| City of Philadelphia |
2000 |
Census bureau statistical units. Most commonly used statistical unit. This version, released January 2004 contains a fix, replacing tract 172 which was omitted from previous versions.
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| City of Philadelphia |
2010 |
Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. Census tracts generally have between 1,500 and 8,000 people, with an optimum size of 4,000 people. Counties with fewer people have a single census tract.) When first delineated, census tracts are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over many decades so that statistical comparisons can be made from decennial census to decennial census. However, physical changes in street patterns caused by highway construction, new developments, and so forth, may require occasional boundary revisions. In addition, census tracts occasionally are split due to population growth or combined as a result of substantial population decline.Census tracts are identified by a four-digit basic number and may have a two-digit numeric suffix; or example, 6059.02. The decimal point separating the four-digit basic tract number from the two-digit suffix is shown in the printed reports and on census maps. In computer-readable files, the decimal point is implied. Many census tracts do not have a suffix; in such cases, the suffix field is either left blank or is zero-filled. Leading zeros in a census tract number (for example, 002502) are shown only in computer-readable files. Census tract suffixes may range from .01 to .98. For the 1990 census, the .99 suffix was reserved for census tracts/block numbering areas (BNAs) that contained only crews-of-vessels population; for Census 2000, the crews-of-vessels population is part of the related census tract.Census tract numbers range from 1 to 9999 and are unique within a county or statistically equivalent entity. The U.S. Census Bureau reserves the basic census tract numbers 9400 to 9499 for census tracts delineated within or to encompass American Indian reservations and off-reservation trust lands that exist in multiple states or counties. The number 0000 in computer-readable files identifies a census tract delineated to provide complete coverage of water area in territorial seas and the Great Lakes. Projection: Lambert Conformal ConicXY Coordinate System: NAD 1983 StatePlane Pennsylvania South FIPS 3702 (US Feet)Datum: NAD 1983Units of Measurement: Foot_US
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| City of Philadelphia |
2025 |
Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. Census tracts generally have between 1,500 and 8,000 people, with an optimum size of 4,000 people. Counties with fewer people have a single census tract.) When first delineated, census tracts are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over many decades so that statistical comparisons can be made from decennial census to decennial census. However, physical changes in street patterns caused by highway construction, new developments, and so forth, may require occasional boundary revisions. In addition, census tracts occasionally are split due to population growth or combined as a result of substantial population decline.Census tracts are identified by a four-digit basic number and may have a two-digit numeric suffix; or example, 6059.02. The decimal point separating the four-digit basic tract number from the two-digit suffix is shown in the printed reports and on census maps. In computer-readable files, the decimal point is implied. Many census tracts do not have a suffix; in such cases, the suffix field is either left blank or is zero-filled. Leading zeros in a census tract number (for example, 002502) are shown only in computer-readable files. Census tract suffixes may range from .01 to .98. For the 1990 census, the .99 suffix was reserved for census tracts/block numbering areas (BNAs) that contained only crews-of-vessels population; for Census 2000, the crews-of-vessels population is part of the related census tract.Census tract numbers range from 1 to 9999 and are unique within a county or statistically equivalent entity. The U.S. Census Bureau reserves the basic census tract numbers 9400 to 9499 for census tracts delineated within or to encompass American Indian reservations and off-reservation trust lands that exist in multiple states or counties. The number 0000 in computer-readable files identifies a census tract delineated to provide complete coverage of water area in territorial seas and the Great Lakes. Projection: Lambert Conformal ConicXY Coordinate System: NAD 1983 StatePlane Pennsylvania South FIPS 3702 (US Feet)Datum: NAD 1983Units of Measurement: Foot_US
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| City of Philadelphia |
2016 |
This polygon layer represents the Market Areas for each representative from the Department of Commerce Office of Business Services (OBS). All polygons (nearly all of which correspond to zip codes) associated with an OBS representative’s name comprise that representative’s Market Area.
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| City of Philadelphia |
2016 |
This data set reflects the recipients, award amounts, and project sites for grant money disbursed by the Philadelphia Commerce Department for the Storefront Improvement Program whereby businesses are provided the funds to improve the exterior of their storefront and beautify the commercial corridor on which they operate.
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| City of Philadelphia |
2016 |
correctional facility points in the City, administered by the Philadelphia Prisons System
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| City of Philadelphia |
1990 |
District boundaries of municipal legislators.
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| City of Philadelphia |
2000 |
District Boundaries of Municipal Legislators
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| City of Philadelphia |
2024 |
This map data layer represents the city council districts for the City of Philadelphia, PA. The city council districts data layer was determined by the boundaries of the Philadelphia County voting precincts and the City Ordinance that established the districts based on the precincts.
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| City of Philadelphia |
2024 |
This dataset is composed of the entire City of Philadelphia's parcels based on their legal descriptions.
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| City of Philadelphia |
2012 |
Commercial/industrial zones with special amenities.
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| City of Philadelphia |
2012 |
Commercial/industrial zones with special amenities
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| City of Philadelphia |
2016 |
Philadelphia Fire Department Fire Station Locations.
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| City of Philadelphia |
2016 |
Polygon boundaries of geographic market areas in the city of Philadelphia.
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| City of Philadelphia |
2025 |
Point geometric features representing planned and complete Green Stormwater Infrastructure (GSI).
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| City of Philadelphia |
2025 |
Philadelphia Green stormwater infrastructure public projects on parcels for the Big Green Map
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| City of Philadelphia |
2013 |
Green City, Clean Waters is Philadelphia's 25-year plan to protect and enhance our watersheds by managing stormwater with green infrastructure. This feature class represents Green Stormwater Infrastructure (GSI) locations that are currently in the design phase or have been constructed.
Features updated: 01/25/2013
Attributes updated: 01/25/2013
Metadata updated 01/25/2013
Update Frequency - monthly
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| City of Philadelphia |
2015 |
Planimetric Coverage containing the delineation of impervious surfaces for studying and calculating drainage runoff. This coverage shows surface features that are visible on the aerial photography, and is sometimes referred to as the landbase.
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| City of Philadelphia |
2008 |
High resolution land cover dataset for Philadelphia. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at ten square feet. The primary sources used to derive this land cover layer were 2008 Orthophotography and 2008 LiDAR LAS data. Ancillary data sources included GIS data (building footprints, road polygons, and hydrography) provided by City of Philadelphia. This land cover dataset is considered current as of 2008. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. More than 30700 corrections were made to the classification.
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| City of Philadelphia |
2018 |
High resolution land cover dataset for Philadelphia,Pennsylvania. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2018 LiDAR data and 2017 NAIP imagery. Ancillary data sources included GIS data provided by Philadelphia,Pennsylvania or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3500 and all observable errors were corrected.
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| City of Philadelphia |
2012 |
Free Library of Philadelphia locations (branches and Central) are represented as point features. Feature Update Date: 05-2012
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| City of Philadelphia |
2008 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2008. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
2010 |
LiDAR data collection performed over the City of Philadelphia, PA in April of 2010. Products generated include Breaklines, 10ft DEM and 5ft DEM.
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| City of Philadelphia |
2015 |
This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2018 |
2018 LiDAR – 8ppm – Classified. LiDAR and LAS data was gathered for the City of Philadelphia in April 2018. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. Key attribute field names and descriptions: LiDAR Classification Categories: 0 Created Not Classified 1 Unclassified 2 Ground 3 Low vegetation 4 Vegetation 5 High vegetation 6 Building 9 Water 17 Bridge Deck
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| City of Philadelphia |
2022 |
LiDAR and LAS data was gathered for the City of Philadelphia in April 2022. DEMs were generated from the raw data. This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 239 sq miles total. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. At the time of capture ground conditions were leaf-off, snow free, and water was at normal levels. For Additional Information see: https://www.pasda.psu.edu/download/phillyLiDAR/2022/Metadata_and_Reports/Lidar_Report/65221207_Philadelphia_Mapping_Report.pdf
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| City of Philadelphia |
2015 |
Classified LiDAR LAS and Derivative Products - This dataset is lidar point cloud data covering the City of Philadelphia, PA, approximately 196 sq miles total. The dataset consists of 1024 lidar point cloud LAS files. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. The data was collected at a nominal point spacing of 0.70m using Pictometry's Riegl LMS-Q680i LiDAR system over 4 mission days on April 18th, 19th, 22nd, and 25th, 2015. At the time of capture ground condiitons were leaf-off, snow free, and water was at normal levels.
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| City of Philadelphia |
2025 |
The Litter Index is used to compare the relative cleanliness of different areas of the city of Philadelphia.
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| City of Philadelphia |
2025 |
This file features polylines representing the major trails in the Philadelphia Parks & Recreation (PPR) system. It is not comprehensive of all trails thoughout the system. Other minor and rogue trails do exist and are not represented here. Data Development:Data were created by PP&R staff using historic information from the Fairmount Park Commission and Department of Recreation data archives. The data was primarily sourced using GPS technology. In certain cases data were created through orthophotography, georeferenced civil plans, and hard copy mapsTrail data is updated on a regular basis to indicate new trails, closed trails, and trails undergoing restoration or expansion.
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| City of Philadelphia |
2000 |
District boundaries of municipal legislators.
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| City of Philadelphia |
2025 |
This feature class is the administrative boundaries for the Philadelphia Police Department's districts.
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| City of Philadelphia |
2025 |
This feature class is the administrative boundaries for the Philadelphia Police Department's divisions. Divisions are subdivided into districts.
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| City of Philadelphia |
2025 |
There are currently 65 Police Service Areas (PSA) boundaries in Philadelphia with two to four per District. These boundaries replaced a much smaller boundary, Sectors in 2009.
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| City of Philadelphia |
2016 |
The Center City District (CCD) is a business improvement district. The mission is to keep Center City clean, safe, and fun. CCD also makes phyiscal improvements to center city by installing and maintain lighting, signs, banners trees and landscape.
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| City of Philadelphia |
2016 |
This layer displays their policing boundary for the Center City District (CCD) business improvement district.
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| City of Philadelphia |
2016 |
This feature class contains polygons that illustrate 10 correctional facilities in the City, adminstered by Philadelphia Prisons System.
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| City of Philadelphia |
2016 |
This feature class contains points that represent 10 correctional facilities in the City, administered by Philadelphia Prisons System.
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| City of Philadelphia |
2016 |
Point data of all First Judicial District of PA courts.
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| City of Philadelphia |
2016 |
This data was developed for cartographic use -- specifically, as reference information for the Police Athletic League.
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| City of Philadelphia |
2025 |
Point locations of the Police District Headquarters in Philadelphia
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| City of Philadelphia |
2025 |
The shapefile is the administrative boundaries for Police Sectors. Sectors are the logical
means by which police districts are subdivided. Each district is made up of 12-30 sectors.
In theory, a police patrol car is assigned to each sector and a supervisor for each 3-4 sectors.
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| City of Philadelphia |
2016 |
Political subdivisions. Feature Update Date: 09-2012
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| City of Philadelphia |
2016 |
Polygon boundaries of PPR's operational districts as established by PPR's GIS staff and reviewed, revised, and approved by PPR's executive staff.
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| City of Philadelphia |
2009 |
This is a boundary file identifying Philadelphia development certified areas
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| City of Philadelphia |
2012 |
Historic Sites on the National Register. Updated by the Planning Commission in Fall, 2010.
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| City of Philadelphia |
2012 |
Historic Sites on the Philadelphia Register. Updated by the Planning Commission in Fall, 2010.
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| City of Philadelphia |
2016 |
The location of schools in the City of Philadelphia with attribute information for address, grade level, type, and status indicating whether the school is open, proposed for closure or closed. The status field is intended to provide up to date information on school facility management conducted by all institution types.KEY ATTRIBUTE FIELDSGradeLevel – generalized description of grade levels as elementary, middle or high schoolGradeOrg – Grade Organizational Levels - estimate of grade levels range at the school, e.g., K-5Instit_Type – Institutional Type - public, private, parochial or charterActive – The current designation as active, closed or closing with expected closure date. Feature Update Date: 03-2012
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| City of Philadelphia |
2016 |
Inventory of fixed assets owned or leased by the City of Philadelphia including buildings, structures, piers, and properties (not including surplus properties). Also known as the Master Facilities database. Critical infrastructure omitted from public version.
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| City of Philadelphia |
2016 |
This layer contains the railroad line capture from 2004 orthophotography. Sanborn’s methods for capturing railroad lines included aerial imagery and the 2004 DEM.
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| City of Philadelphia |
2004 |
This layer contains the railroad line capture from 2004 orthophotography. Sanborn’s methods for capturing railroad lines included aerial imagery and the 2004 DEM. This is one of the planimetric coverages developed as part of the aerial survey project of 1996 and updated using new aerial photography collected between 25 March 2004 and 23 April 2004.
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| City of Philadelphia |
2016 |
information on solar technology installations across Philadelphia.
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| City of Philadelphia |
2025 |
Network of streets within City of Philadelphia with bike lanes and/or bike-friendly markings.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Bridge Division in maintaining and referencing the bridges of the City of Philadelphia.
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| City of Philadelphia |
2016 |
This layer identifies the point locations of the city owned bridges that are maintained by the Bridge Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer identifies the 286 city plan boundaries for the Surveys Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
Edge of drivable pavement, or travelway for all drivable pavement citywide, including the edge of traffic flow islands and drivable pavement within parks
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| City of Philadelphia |
2016 |
Polygons representing the area of drivable pavement citywide, as well as interior non-street polygons.
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| City of Philadelphia |
2016 |
Just internal polygons with "FCODE = 9999" representing the area of drivable pavement citywide, as well as interior non-street polygons.
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| City of Philadelphia |
2016 |
This layer delineates the six districts of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the six districts of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the fifty-six sections of the Highway Division of the City of Philadelphia Streets Department. Sections can be aggregated into districts and subdivided into subsections.
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| City of Philadelphia |
2016 |
This layer delineates the 703 subsections of the Highway Division of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer delineates the Arcs of the Polygons of the 703 subsections of the Highway Division of the City of Philadelphia Streets Department. Subsections can be aggregated into sections and districts.
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| City of Philadelphia |
2016 |
Street segments listed on the Philadelphia Register of Historic Places as part of the Historic Street Paving Thematic District, as amended by the Historical Commission December 12th, 2014.
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| City of Philadelphia |
2016 |
Polygons for street segments listed on the Philadelphia Register of Historic Places as part of the Historic Street Paving Thematic District, as amended by the Historical Commission December 12th, 2014.
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| City of Philadelphia |
2016 |
This layer identifies the active intersection controls for the Street Lighting and Traffic Engineering Divisions of the City of Philadelphia Streets Department.
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| City of Philadelphia |
2016 |
This layer identifies boundaries for City Leaf Collection Services.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
To define the boundaries for Leaf Collection within the City of Philadelphia
The arc layer contains street name attributes for labeling the outside of the polygons. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
This layer identifies boundaries for City Leaf Collection Services.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
The Litter Index is used to compare the relative cleanliness of different areas of the city of Philadelphia.
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| City of Philadelphia |
2016 |
To map streets with no through trucks in the City of Philadelphia.
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| City of Philadelphia |
2016 |
City of Philadelphia paving plan for 2015
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| City of Philadelphia |
2016 |
The Recycling Diversion Rate is the rubbish collection tonage divided by the recycling collection tonnage by sanitation on collection day.
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| City of Philadelphia |
2025 |
The data is used to determine the day of collection for a given location and set of households in the City of Philadelphia. The file is also used to aggregate data such as households, tonnage, and mileage.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation areas containing line, point, and polygon data used by the City of Philadelphia.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation districts containing line, point, and polygon data used by the City.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
Coverage of all the sanitation districts containing line, point, and polygon data used by the City.
The arc layer contains data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2024 |
Used citywide as base layer for many purposes/applications. The street centerline is available for reference purposes only and does not represent exact engineering specifiactions. The Philadelphia Streets Department makes no guarantees as to the accuracy of the layer.
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| City of Philadelphia |
2016 |
Created to show the City of Philadelphia's street lighting route districts.
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| City of Philadelphia |
2016 |
The street nodes layer was developed for use by agencies citywide including PWD, PCPC, Police, BRT, Health, etc.
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| City of Philadelphia |
2025 |
This layer was developed to aid the Street Lighting Division in planning, referencing, and maintaining the active street poles within the City of Philadelphia. Examples include: providing information regarding group replacement projects and any individual edits, using tables from layer for billing, and aiding cityworks.
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| City of Philadelphia |
2016 |
The Arterial layer was developed to aid various city agencies with planning, organizing, and maintaining the streets of the City of Philadelphia. These agencies include PWD, PCPC, Police, BRT, Health, etc.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
This is the arc file of the Traffic Districts layer. It was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
2016 |
This layer was developed to aid the Traffic Division in planning, organizing, and maintaining traffic flow within the City of Philadelphia. Examples include: the maintenance and placing of stop signs and signals and monitoring street travel direction.
Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
Big Belly brand waste baskets maintained/collected by the City of Philadelphia.
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| City of Philadelphia |
2016 |
Non Big Belly waste baskets maintained/collected by the City of Philadelphia
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| City of Philadelphia |
2016 |
The purpose of this dataset is to represent the Zip Code areas for the City of Philadelphia. The edges of Zip Codes are slightly modified for logical and cartographic purposes.
The arc layer contains data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption.
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| City of Philadelphia |
2016 |
Philadelphia. The edges of Zip Codes are slightly modified for logical and cartographic purposes.
This polygon layer has an accompanying arc layer. Certain arcs in the arc layer contain data signifying information relating it to the polygon layer. It can tell you if both sides of the arc belong to one of the bounding polygons. All the arcs, including those with no boundary info, have naming attributes for labeling the polygon borders. Contact the Streets GIS unit for public consumption of the corresponding arc layer.
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| City of Philadelphia |
1996 |
Planimetric Coverage containing the delineation of topographic contours. Annotation of Contour Line elevations exists. Annotation viewable at 1" = 200?
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| City of Philadelphia |
2004 |
This dataset is composed of the entire City of Philadelphia's transparcels based on their legal descriptions. Feature update 2004
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| City of Philadelphia |
2025 |
This point layer represents dams. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This layer represents Green Stormwater Infrastructure Stormwater Management Practice types. Integrating Green Stormwater Infrastructure (GSI) into a highly developed area such as Philadelphia requires a decentralized and creative approach to planning and design. Various tools can be implemented to accomplish this, including stormwater planters, rain gardens and green roofs. All of these tools help to reduce runoff volume and filter pollutants by intercepting stormwater runoff before it enters the City's combined sewer system. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This point layer contains all the wastewater and stormwater inlet locations. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
This point layer contains all the stormwater outfalls. The Purpose of this data is to describe the asset both locationally and via its attributes which are extensive for a GIS dataset and which are maintained. This data will serve as a platform for planning, analysis and research at PWD.
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| City of Philadelphia |
2025 |
The primary purpose of PWD_PARCEL layer is to calculate parcel-based stormwater charges for PWD customers under the new parcel-based stormwater billing program. The layer was created from the DOR_PARCELS layer in 2005 after it was decided that none of the other City parcel layers could meet the needs of PWD's stormwater billing program. Those needs are generally that the parcel delineations match up to what people actually own, that there is an accurate assessment of the impervious area on the parcel, and that there is owner information associated with the parcel. Over the past 5 years, PWD has made corrections based off deeds on file with DOR, BRT information, and other City records. PWD also matched up each DOR parcel to a corresponding BRT record that contained the owner information for that parcel.
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| City of Philadelphia |
2025 |
The purpose of this data is to describe the Rain Gauges both locationally and via their attributes.
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| City of Philadelphia |
2016 |
Combined Sewer Service area within Philadelphia
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| City of Philadelphia |
2016 |
Green City, Clean Waters is Philadelphia's 25-year plan to protect and enhance our watersheds by managing stormwater with green infrastructure. This feature class represents Green Stormwater Infrastructure (GSI) locations that are currently in the design phase or have been constructed.
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| City of Philadelphia |
2016 |
District planning areas for Green City, Clean Waters stormwater management strategic planning.
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| City of Philadelphia |
2016 |
Point geometric features representing land development projects in compliance with PWD’s Stormwater Regulations.
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| City of Philadelphia |
2016 |
Philadelphia streams as mapped by Charles Ellet in 1842 and Previous study of historic streams conducted by PWD.
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| City of Philadelphia |
2016 |
Philadelphia shoreline of Delaware River and Schuylkill River, digitized as mapped by Charles Ellet in 1842. Data is digitized from georeferenced map scans.
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| City of Philadelphia |
2025 |
Polyline geometric features representing the center flow line of all waterways in Philadelphia's five major watersheds.
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| City of Philadelphia |
2025 |
Polygon geometric features representing the waterways and impoundments in Philadelphia's five major watersheds.
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| City of Philadelphia |
2025 |
Polygon feature class representing major watersheds in the Philadelphia and Region.
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| City of Philadelphia |
2025 |
Polygon feature class represtenting major watersheds in the Philadelphia Region.
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| City of Philadelphia |
2016 |
Rain Barrel installation locations prior to RainCheck.
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| City of Philadelphia |
2025 |
RainCheck program installation sites
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| City of Philadelphia |
2016 |
Hydrographic polygon features included in Philadelphia Hydrology Map. This map was officially adopted by City Council as the official map of Philadelphia Watercoures Designated for Protection on September 13th, 2012. The geographic data depicts watercourses within Philadelphia County as they appear on the map and will not be edited or updated.
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| City of Philadelphia |
2016 |
Hydrographic polygon features included in Philadelphia Hydrology Map. This map was officially adopted by City Council as the official map of Philadelphia Watercoures Designated for Protection on September 13th, 2012. The geographic data depicts watercourses within Philadelphia County as they appear on the map and will not be edited or updated.
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| City of Philadelphia |
2016 |
The Zoning Code establishes a registry of community organizations to be managed by the Philadelphia City Planning Commission. The RCO system is designed to improve community notification of proposed developments and make community involvement more predictable across the city. RCOs became effective on August 22, 2012. Polygon boundaries of Registered Community Organizations (RCO) as established under Section 14-303 (12) of the City of Philadelphia Zoning Code enacted December 15, 2011 and made effective August 22, 2012. RCO’s register annually with the City Planning Commission and are notified of projects requiring Zoning Board approval or Civic Design Review. RCO boundaries may overlap as permitted by the Zoning Code.KEY ATTRIBUTE FIELDS:RCO Name: Name of Registered Community OrganizationType: Type of RCO.Expire Date: Date RCO registration with PCPC expiresExpire Year: Year RCO registration with PCPC expires Philadelphia Zoning-Related Websites:Registered Community Organizations -- http://www.phila.gov/CityPlanning/projectreviews/Pages/RegisteredCommunityOrganizationsZoning Code -- http://www.amlegal.com/library/pa/philadelphia.shtml and navigate to Title 14Zonng Map -- http://www.phila.gov/map On pop-up menu scroll down to 'Zoning'Zoning Administration Manual -- http://www.phila.gov/CityPlanning/projectreviews/Pages/Zoning.aspxDATA DEVELOPMENT:Features produced in ArcGIS Desktop using PA South Stateplane coordinates, NAD83, US Foot. Boundaries were constructed by PCPC using self-reported geographic descriptions by the RCO. Feature Update Date: 01-2013
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| City of Philadelphia |
2024 |
Geopolitical Areas
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| Columbia County |
2024 |
Roads of Columbia County, Pennsylvania
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| Columbia County |
2024 |
This dataset contains Address Points in Columbia County, Pennsylvania
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| Columbia County |
2024 |
Tax Parcels of Columbia County, Pennsylvania
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| Columbia County |
2018 |
BLS (Basic Life Support - EMT) ambulance coverage within Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania address points. Location and information of address points in Crawford County,Pennsylvania. This feature service has restricted fields available for this open data version. Certain fields have been redacted. The full dataset (as seen via the GIS mapping applications) is available via cost by contacting Crawford County. The full dataset available at cost provides all records that are not redacted by law. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Location of aiports and information about them within Crawford County PA
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| Crawford County |
2018 |
Advanced Life Support (ALS) / paramedic service coverage within Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania ID Points. ID points compliment the tax parcel layer and provide the location of subdivisions (general area) and individual lots. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Campgrounds location and individual site information within Crawford County, PA
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| Crawford County |
2018 |
Locations of cemeteries, and access road information
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| Crawford County |
2018 |
Fire department area coverage in Crawford County, PA
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| Crawford County |
2018 |
Lake boundaries were digitized from various years of ortho-imagery
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| Crawford County |
2020 |
Location of landmarks and places of interest within Crawford County, PA
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| Crawford County |
2018 |
Crawford County PA municipal election precincts areas
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| Crawford County |
2024 |
Tax parcels for Crawford County PA. This information is constantly updated and maintained by the Crawford county assessment office (814-333-7302) or gisassessment@co.crawford.pa.us
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| Crawford County |
2018 |
Voting districts for the PA House of Representatives within Crawford County PA
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| Crawford County |
2018 |
PA Senate voting districts within Crawford County PA
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| Crawford County |
2018 |
Municipal and State Police reponse areas
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| Crawford County |
2020 |
Polling place locations and information within Crawford County PA
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| Crawford County |
2018 |
Locations of Fire, EMS,and Police stations and other miscelanous Public Safety agencies in Crawford County, PA
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| Crawford County |
2018 |
QRS (Quick Response Service) coverage areas within Crawford County, PA. QRS service is provided by fire departments to provide basic care until a BLS or ALS ambulance service can arrive. These are commonly found in areas in which a department doesn't run BLS/ALS services.
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| Crawford County |
2018 |
Location and information of railroad tracks in Crawford County PA. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
Location and information of recreational public trails used for hiking, biking, walking, running within Crawford County, PA
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| Crawford County |
2018 |
Rescue department coverage areas within Crawford County, PA
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| Crawford County |
2018 |
Rivers/streams digitized from various years of ortho-imagery
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| Crawford County |
2018 |
School district coverage areas in Crawford County, PA
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| Crawford County |
2018 |
Crawford County, Pennsylvania softlines. Softlines compliment the tax parcel layer and show information such as (not limited to) land hooks, streams, railroads, lot lines, free hooks. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2024 |
Crawford County, Pennsylvania street centerline. Street centerline location and information within Crawford County, Pennsylvania. This is a feature service and data can change at anytime without notice.
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| Crawford County |
2018 |
US Congressional voting districts within Crawford County, PA
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| Crawford County |
2018 |
US Senate Voting Districts within Crawford County, PA
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| Crawford County |
2023 |
Address Points for Cumberland County, PA. These represent addressable structures.
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| Cumberland County |
2023 |
Hiking and multi-use land trails in Cumberland County, Pennsylvania.
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| Cumberland County |
2021 |
Real estate boundaries and parcel identification number for Cumberland County, Pennsylvania. These boundaries do not represent survey accuracy
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| Cumberland County |
2023 |
Public parks located in Cumberland County, Pennsylvania.
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| Cumberland County |
2023 |
Real estate boundaries and parcel identification number for Cumberland County, Pennsylvania. These boundaries do not represent survey accuracy
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| Cumberland County |
2022 |
The parcels are intended to provide a graphical representation of deeded properties in Dauphin County and be used for basemapping, thematic mapping and analysis.
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| Dauphin County |
2022 |
Road centerlines for Dauphin County. Used for the emergency dispatch system, mapping, and reference.
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| Dauphin County |
2022 |
Trails of Delaware County, Pennsylvania
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| Delaware County |
2021 |
Protected Lands of Delaware County, Pennsylvania
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| Delaware County |
2023 |
This dataset contains street centerlines for vehicular and foot traffic in Delaware County, Pennsylvania
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| Delaware County |
2023 |
This polygon layer represents School District boundaries of Delaware County, Pennsylvania
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| Delaware County |
2023 |
This layer includes all of the current sidewalks of Delaware County, Pennsylvania
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| Delaware County |
2023 |
Municipal Zoning of Delaware County, Pennsylvania
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| Delaware County |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the DVRPC - PA project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2016 |
Delaware Valley 2015 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of UTM Zone 18, NAD83 (2011), meters and vertical datum of NAVD1988 (GEOID12A), meters. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 25 individual 1500 meter X 1500 meter tiles for the pilot (3752 individual 1500 meter X 1500 meter tiles for the entire project area), Bare Earth DEMs tiled to the same 1500 meter X 1500 meter tile schema, and Breaklines in Esri shapefile format. Ground Conditions: LiDAR was collected in spring of 2015, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established 76 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the Delaware Valley project area. The accuracy of the data was checked with 91 NVA points and 70 VVA points (161 total QC checkpoints).
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| Delaware Valley Regional Planning Commission |
2020 |
This orthoimagery consists of 1-foot pixel resolution, 3-band, natural color county mosaics in JPEG 2000 format covering the Delaware Valley Regional Planning Commission’s (DVRPC) 9-county region (Bucks, Chester, Delaware, Montgomery, and Philadelphia counties in Pennsylvania; and Burlington, Camden, Gloucester, and Mercer counties in New Jersey). This orthoimagery was acquired in the late winter/early spring of 2020. An orthoimage is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map.
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| Delaware Valley Regional Planning Commission |
2025 |
Boundary outlines of individual properties in Erie County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Erie County |
2024 |
The geographic extent of individual parcels within Franklin County with NO associated CAMA (Computer Assisted Mass Appraisal - tax base) or parcel information. Polygons derived from original documents (deeds & surveys) by the Sidwell Co in 2013 and maintained since by County GIS staff.
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| Franklin County |
2021 |
GIS dataset for GeoCoding and Routing. Multiple Centerline datasets can be used for GeoCoding as sub-locators can be created and configured within a Composite Locator.
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| Fulton County |
2021 |
Boundary outlines of individual properties in Fulton County.
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| Fulton County |
2021 |
Structures resents all officially addressed and non-addressed structures in Fulton County, PA. Addressed structures may include but not limited to buildings of residence, work, or recreation
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| Fulton County |
2022 |
Access Lines represents access/drive ways to 911 addressed structures in Huntingdon County, PA. These features typically represent un-named driveways, right of ways, or paths that may be used by 911 services in the case of an emergency. Changes to this data are updated on a weekly basis, or at the county’s discretion. Beginning with December 2022 this data follows the NG911 schema.
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| Huntingdon County |
2022 |
"AddressPoints" was created to support Huntingdon County, PA in its mission to serve government, public, and private entities. This layer is maintained by Huntingdon County Mapping Dept. for the county PSAP (Public Saftey Answering Point, 911) to assist in routing emergency service calls. "AccessLines" is also used by Huntingdon County offices to assist in day to day office/field work.
Beginning with December 2022 this data follows the NG911 schema.
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| Huntingdon County |
2022 |
Roads represents all offical road center lines in Huntingdon County, PA. These features represent all Huntingdon County roads: federal & state, municipal & borough, public & private.The original data was derived from PennDOT road centerlines and expanded by the Huntingdon County Planning and Mapping departments. Currently the data is maintained and updated by the Mapping Department on a weekly basis, or at the county’s discretion.
Beginning with December 2022 this data follows the NG911 schema
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| Huntingdon County |
2023 |
Centerlines
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| Indiana County |
2023 |
Major Roads
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| Indiana County |
2023 |
Lakes
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| Indiana County |
2023 |
Municipal Boundaries
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| Indiana County |
2023 |
Parcels
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| Indiana County |
2023 |
Railroads
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| Indiana County |
2008 |
Boundaries of municipalities within Juniata County, Pennsylvania
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| Juniata County |
2024 |
Polygon coverage representing Juniata County, Pennsylvania properties
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| Juniata County |
2024 |
Points of addressable structures within Juniata County, Pennsylvania
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| Juniata County |
2024 |
Line coverage representing Juniata County, Pennsylvania street centerlines
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| Juniata County |
2008 |
The data describes the boundary of the Lancaster County.
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| Lancaster County |
2008 |
This layer consists of polygons representing bridges in Lancaster County. It contains both covered and non-covered bridges.
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| Lancaster County |
2018 |
This layer polygon coverage that depict the outlines of buildings larger than 10 feet by 10 feet in size as captured from aerial photography. Includes primary structures such as residential, agricultural, business, and industrial and large ancillary structures such as barns, detached garages, out buildings, storage sheds, silos, etc.
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| Lancaster County |
2008 |
This polygon coverage includes both recent and historical cemeteries. County supplied source data to identify historical locations. Dataset consists of all cemeteries which were identifiable in the 1993 photography, plus historical cemeteries which were mapped using other source data and or verification methods.
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| Lancaster County |
2008 |
This dataset consists of lines representing elevation contours at a 5 foot interval, with index contours at a 25 foot interval.
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| Lancaster County |
2008 |
Polygons within this coverage represent the outlines of each covered bridge in Lancaster County, PA, as determined via aerial photographs.
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| Lancaster County |
2019 |
Polygons representing photogrammetrically interpreted edges of paved and unpaved drives greater than 200 ft. in length. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2008 |
Surveyed positions of monuments for geodetic control.
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| Lancaster County |
2014 |
Hydrography Centerlines. This dataset contains lines representing streams and the centerlines of rivers wider than 10 feet. The features are pointed in the direction of streamflow.
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| Lancaster County |
2014 |
Hydrography Polygons. This dataset contains areas representing water bodies and rivers wider than 10 feet.
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| Lancaster County |
2022 |
Polygon coverage representing the predominant land use or land cover, as interpreted from aerial photography. Note: GDI delineated the land use boundaries using aerial photographs dated March 1993 and using a minimum mapping unit of one-half acre. Based on acquired aerial photography, subsequent updates have been made using aerial photos from: 1998, 2002, 2005, 2008, 2012, 2016, 2018. ADR compiled land use polygons for all surface water features. NWI wetlands areas were identified using boundaries from a Quad-based wetland coverage; the wetland boundaries were then delineated using the aerial photography. Land uses are categorized using the Modified Anderson Level II classification system. Quality control check plots were produced at 1"=250' scale the whole county. Planning Commission staff reviewed all of the check plots and forwarded comments and corrections to GDI. GDI made some of the corrections but did not make others, claiming that they were below the half-acre minimum mapping unit limit. Planning Commission staff has also made corrections where they have been found.
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| Lancaster County |
2024 |
Polygon layer representing the Lancaster County municipal boundaries, as mapped by the assessment office.
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| Lancaster County |
2024 |
Polygon layer representing the real Lancaster County property, as mapped by the assessment office.
Data created for Lancaster County Assessment Office. Intended for illustration and demonstration purposes only.
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| Lancaster County |
2019 |
This polygon coverage represents photogrammetrically interpreted surfaces and edges of parking lots. All lots in excess of 1000 square feet will be compiled. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2024 |
Polygon layer representing public parks, public and semi-public recreation land and other conservation land and easements. Based on the tax parcel layer and aerial photographs.
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| Lancaster County |
2008 |
This line coverage contains arc features representing photogrammetrically interpreted Rail
Road centerlines. County Planning Commission supplied reference map to identify active
features from abandoned features.
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| Lancaster County |
2024 |
This line coverage contains arc features representing all photogrammetrically interpreted street centerlines in the county, including roads built during or after 1993. RPT will resolve and generate road names.
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| Lancaster County |
2019 |
Arcs representing photogrammetrically interpreted surfaces and edges of public and private roads. Features were updated using NGA and USGS 30 centimeter aerial photos flown from March 26th through April 3rd, 2012 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2019 |
Polygons representing photogrammetrically interpreted surfaces and edges of public and private roads. Features were updated using Pictometry 6 inch resolution aerial photos flown in the spring of 2016 that encompass the entire County. Red-Green-Blue and Color Infrared were both used to capture, modify, and validate new and changed features.
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| Lancaster County |
2008 |
Boundaries of Lancaster County School Districts
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| Lancaster County |
2024 |
Comprehensive layer of the trail network in Lancaster County. Trails data has been updated to support uploading to the DCNR's http://www.explorepatrails.com/ site Last update was based on 2018 orthophotos.
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| Lancaster County |
2024 |
This a county-wide coverage that contains polygons showing the urban and village growth boundaries in the county. Growth boundaries are split along municipal boundaries.
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| Lancaster County |
2021 |
Abandoned Railroads of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Footprints for buildings in Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Municipal Boundaries of Lehigh County, Pennslyvania
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| Lehigh County |
2021 |
Boundary outlines of individual properties in Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Railroads of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Road Centerlines of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Political Wards of Lehigh County, Pennsylvania
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| Lehigh County |
2021 |
Political Wards of Lehigh County, Pennsylvania
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| Lehigh County |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the LVPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| Lehigh Valley Planning Commission |
2022 |
Luzerne County Municipal boundaries which defines the current municpal configurations based on best available information. Data may also contain various administrative, planning and property data. Best Fit to 1"=200' orthophotography, County Tax Parcels, GPS field work, Provided Surveys, Road Docket Plots and/or other sources.
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| Luzerne County |
2022 |
School District Boundaries of Luzerne County, Pennsylvania
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| Luzerne County |
2025 |
Property or Parcel Boundaries of all known properties in Lycoming County, PA
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2008 |
The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.
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| Lycoming County |
2020 |
Lycoming County UnNamed Roads. These are primarily private roads or driveways, but may contain Commonwealth maintenance roads which do not require a name.
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| Lycoming County |
2020 |
Lycoming County Named Roads. This contains both private and public roads which have an address range and an approved road name as per the Lycoming County Addressing Ordinance 96-3
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| Lycoming County |
2020 |
Addressed Structures or Sites in Lycoming County as per the Lycoming County Addessing Ordinance 96-3
Note: Zip Code values are estimated and not verified.
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| Lycoming County |
2024 |
A Polygon dataset that represents the lands of local airport
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| Mercer County |
2024 |
Road segments representing centerlines of all roadways or carriageways in a local government. Typically, this information is compiled from orthoimagery or other aerial photography sources. This representation of the road centerlines support address geocoding and mapping. It also serves as a source for public works and other agencies that are responsible for the active management of the road network.
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| Mercer County |
2024 |
Polygons that represent the total lands of a college or university campus
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| Mercer County |
2024 |
A point data set of the local libraries
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| Mercer County |
2024 |
A point data set that represents the various municipal buildings
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| Mercer County |
2024 |
The Parcel Dataset was developed primarily for the purpose of identifying land parcels for tax billing and tax assessment purposes.
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| Mercer County |
2024 |
A line dataset of the trails in the greater Mercer County Area
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| Mercer County |
2023 |
Mercer County, Pennsylvania building footprints digitized from 2015 aerial imagery.
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| Mercer County |
2024 |
Mercer County Road Network points
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| Mercer County |
2024 |
set of municipalities derived by tax district numbers, the first 2 digits are parcel pins that denote municipality.
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| Mercer County |
2024 |
Boundary outlines of individual properties in Mifflin County. The Parcel Dataset was developed primarily for the purposes of identifying land parcels for tax billing and tax assessment purposes.
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| Mifflin County |
1995 |
Mifflin County aerial photography
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| Mifflin County |
2022 |
Boundary outlines of individual properties in Monroe County
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| Monroe County |
2022 |
This dataset contains street centerlines for vehicular and foot traffic in Monroe County, Pennsylvania
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| Monroe County |
2024 |
This layer shows site structure address points in Montgomery County, Pennsylvania. Site Structure Address Points (SSAP) represent a physical address of a structure. This address may differ from a mailing address and even a property parcel address. The layer is was developed in accordance with NENA NG911 guidelines. The layer also contains trail and highway mile markers which are needed for 911 location services.
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| Montgomery County |
2024 |
This layer shows the building outlines for Montgomery County, Pennsylvania. The original layer was purchased from NearMap in the spring of 2020. Updates and additions to the original layer were made using parcel and current spring 2022 aerial imagery.
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| Montgomery County |
2022 |
This is Montgomery County's geographic boundary. The original source data has been edited to best match orthophotography, natural features and other known boundary markers. This data is for reference purposes only.
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| Montgomery County |
2022 |
Electric Service Areas
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| Montgomery County |
2022 |
EMS Ambulance Districts
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| Montgomery County |
2022 |
EMS Ambulance Stations
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| Montgomery County |
2022 |
Fire Districts
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| Montgomery County |
2022 |
Fire Stations
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| Montgomery County |
2022 |
Historical Attraction
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| Montgomery County |
2022 |
Hospitals
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| Montgomery County |
2022 |
Libraries
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| Montgomery County |
2022 |
Municipal Boundaries
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| Montgomery County |
2024 |
Parcel boundaries and property information for Montgomery County, Pennsylvania. This data is a download from the county's assessment database and is updated monthly.
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| Montgomery County |
2022 |
Parks
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| Montgomery County |
2022 |
Police Districts
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| Montgomery County |
2022 |
Police Stations
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| Montgomery County |
2022 |
School Districts
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| Montgomery County |
2022 |
Schools
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| Montgomery County |
2022 |
Sewer Service Areas
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| Montgomery County |
2023 |
Street Centerline
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| Montgomery County |
2022 |
Trails
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| Montgomery County |
2022 |
Water Service Areas
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| Montgomery County |
2024 |
Geopolitical Areas
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| Montour County |
2024 |
Roads of Monroe County, Pennsylvania
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| Montour County |
2024 |
This dataset contains Address Points in Monroe County, Pennsylvania
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| Montour County |
2024 |
Tax Parcels of Monroe County, Pennsylvania
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| Montour County |
1999 |
This layer includes the parcels within the municipality along with condominiums and mobile homes. Also included, are the county's tax data along with municipal data such as street address, lot number and subdivision, build out status, land use, zoning, septic or sewer, and housing starts on two year intervals since 1990.
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| Municipality of Murrysville |
1999 |
The Zoning coverage is a polygon layer reflecting Murrysville's zoning districts and their boundaries. The coverage is designed to overlay the parcel coverage, aiding in the initial determination of a parcel or plan's zoning or zonings. Additional attribute data such as setbacks and minimum lot size are included.
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| Municipality of Murrysville |
2003 |
The streets coverage represents all local roads within the Municipality of Murrysville. Where applicable, roads are defined by their local names rather than state and township route numbers. Address ranges are also included.
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| Municipality of Murrysville |
2023 |
PaGS assembled 214,851 relevant well records from Pennsylvania, GroundWater Information System (PaGWIS) and other unpublished PaGS reports. Each well used in the analysis contains a measurement of the depth to bedrock (in feet) and/or a notation indicating if bedrock was encountered during well drilling, these attributes allow well records to be separated into two datasets – bedrock wells (wells that penetrate bedrock) and drift wells (wells that did not encounter bedrock).Topographic Position Index (TPI) is a quantitative landform analysis that uses land surface elevation data to determine landforms such as ridge, upper slope, middle/flat slope, lower slope, and valley. A composite TPI raster for each of Pennsylvania’s 23 physiographic sections was generated. Each well data point was attributed to a physiographic section and assigned a TPI value based on its location. The square root of depth-to-bedrock was calculated for each well. A linear regression relationship between the TPI and the square root of sediment thickness was established for five TPI classes (ridge, upper slope, middle/flat slope, lower slope, and valley) in each of the 23 physiographic sections. This statistical relationship was used to create a surrogate model for depth to bedrock to predict sediment thickness across the state. Synthetic data points were generated from the surrogate model to fill in areas of low well data density. A combination of bedrock well data points and synthetic data points were used to generate the first-iteration sediment thickness model through a natural neighbor interpolation technique. Iterative refinements to the sediment thickness model were made by comparing model predictions to drift well data points. If the total depth of the drift well was less than the predicted thickness of sediment at that location, then the drift well data point was ignored. If the total depth of a drift well was greater than the predicted thickness of sediment at that location, then the drift well data point was added to dataset and a new sediment thickness model was generated. In total, 413, 474 data points were used in the modeling process – 207,130 empirically derived well points and 206, 344 synthetic points derived from the surrogate model.The final sediment thickness model was resampled to a 100-meter grid digital raster conforming to a similar resolution surface topography digital elevation raster. The surface topography grid was smoothed to remove detail before subtracting the sediment thickness to create a bedrock elevation map. The degree of smoothing was applied proportionally to the magnitude of sediment thickness. Portions of the surface topography grid that correspond to sediment thickness greater than 365 feet received the maximum amount of smoothing; likewise, portions of the surface topography grid that correspond to zero sediment thickness received no smoothing. The remaining portions of the surface topography grid that correspond to sediment thickness between 0 and 365 feet received gradational smoothing proportional to the sediment thickness.This 100-meter grid bedrock elevation raster was calculated by subtracting the sediment thickness model from the conditionally-smoothed surface topography digital elevation raster.
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| Pennsylvania Department of Conservation and Natural Resources |
2023 |
An intermediate product of the Pennsylvania Hydrography Dataset (PAHD) generation. This product is the result of a conflation study with existing hydrography originated by the Allegheny County Division of Computer Services Geographic Information Systems Group. This product is not intended to be a finalized component of the Pennsylvania Hydrography Dataset (PAHD): these are provisional data that have undergone no manual refinement. The Modeled_PAHD_Flowpath geometries represent an intermediate product that was created from a workflow that was examining, among other things, the application of conflation steps, monotonicity, and Topographic Positioning Index (TPI) products toward an automated elevation-derived hydrography (EDH) workflow.
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Hydrography layers for the study area covered by the Surficial Geologic Map of Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quads. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of hydrography features of the Bessemer, New Castle South, Portersville, and the Pennsylvania portion of the New Middletown quadrangles
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| Pennsylvania Department of Conservation and Natural Resources |
2024 |
Hydrography layers for the study area covered by Map 24–09.0: Hydrography map showing automated stream permanence identification for the Catawissa 7.5-minute quadrangle, Columbia County, Pennsylvania. The vector data herein were derived from the most recently available Quality Level 2 (QL2) lidar data using geomorphon analysis and least-cost analysis. These data are a subset of the larger Pennsylvania Hydrography Dataset (PAHD), which is in the process of being generated. They were produced using QL2 lidar deliverables and most will have a minimum horizontal accuracy of 1 meter and a minimum vertical accuracy of 0.5 meter at a 1:2,400 scale. This geodatabase contains a dataset of derived hydrography features of the Catawissa quadrangle as well as field validation points collected in the study area.
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| Pennsylvania Department of Conservation and Natural Resources |
2019 |
Pennsylvania North Central Lidar 2019 - Tile Indexes. Includes: LiDAR 2019 PA North 10K QL2, PA North 5K QL1, PA North 5K QL1, North 5K QL2, PA South 10K QL2, PA South 5K QL2
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
2020 |
Pennsylvania Western Lidar 2020 - Tile Indexes. Includes: LiDAR 2020 QL1 5K SP North, QL2 10K SP North, QL2 10K SP South, QL2 5K SP North, QL2 5K SP South
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| Pennsylvania North Central Lidar 2019 - Tile Indexes |
2021 |
Municipal boundaries of Perry County, Pennsylvania
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| Perry County |
2021 |
Parcel boundaries of Perry County, Pennsylvania
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| Perry County |
2025 |
Current Land Use
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| Snyder County |
2025 |
Facility Sites
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| Snyder County |
2025 |
FEMA Flood Zone
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| Snyder County |
2025 |
Municipal Boundary
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| Snyder County |
2025 |
Open Space Conservancy
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| Snyder County |
2025 |
Parcels
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| Snyder County |
2025 |
Polling Places
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| Snyder County |
2025 |
Railroads
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| Snyder County |
2025 |
Road Centerline
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| Snyder County |
2025 |
Site Address Point
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| Snyder County |
2025 |
Voting Precincts
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| Snyder County |
2025 |
Waterbodies
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| Snyder County |
2025 |
Waterlines
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| Snyder County |
2025 |
Zoning District
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| Snyder County |
2024 |
House Numbers of Somerset County, Pennsylvania
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| Somerset County |
2024 |
Street Centerlines of Somerset County, Pennsylvania
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| Somerset County |
2018 |
Building Footprints from Individual Counties for the State of Pennsylvania. This dataset is incomplete and Building Footprints will be added as available. Building Footprints Include: Allegheny, Lancaster, Philadelphia
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| The Pennsylvania State University |
2024 |
Boundary outlines of individual properties for the State of Pennsylvania. This dataset is incomplete and county parcels will be added as available. Attributes contain PIN ID, Source, and Date of Parcels
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| The Pennsylvania State University |
2020 |
Street Centerlines from Individual Counties for the State of Pennsylvania. This dataset is incomplete and Street Centerlines will be added as available.
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| The Pennsylvania State University |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011, 2013, 2016, 2019 and 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.
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| U S Geological Survey |
2021 |
The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de EstadÃstica y GeografÃa), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries.The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements.This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products.The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics.In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 95 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 31 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL1 Intensity Imagery.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the Pennsylvania North Central Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 13813 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 34 counties in Pennsylvania, covering approximately 5621 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
County Mosaics - These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 4 counties in Pennsylvania, covering approximately 85 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 95 individual 5,000 ft x 5,000 ft tiles; and as 31 10,000 ft x 10,000 ft tiled intensity imagery, and as tiled bare earth DEMs; Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the Pennsylvania North Central Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 42 counties in Pennsylvania, covering approximately 14244 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania North FIPS 3701 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 15,404 individual 5,000 ft x 5,000 ft tiles; and 3971 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
Pennsylvania North Central Lidar QL2 Intensity Imagery.
Geographic Extent: 35 counties in Pennsylvania, covering approximately 5922 total square miles.
Dataset Description: The Pennsylvania North Central Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.3. The data were developed based on a horizontal projection/datum of NAD 1983 StatePlane Pennsylvania South FIPS 3702 Feet, Foot US and vertical datum of NAVD88 GEOID12B, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 6,269 individual 5,000 ft x 5,000 ft tiles; 1651 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs.Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring and fall 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 326 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 546 independent accuracy checkpoints, 322 in Bare Earth and Urban landcovers (322 NVA points), 224 in Tall Weeds categories (224 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL1 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL1; Classified Point Cloud
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL1 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 1.25 foot hydro-flattened Raster DEM.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
PA_WesternPA_2019_D20 Lidar QL1 Intensity Imagery.
Geographic Extent: 2 counties in Pennsylvania, covering approximately 62 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL1 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 114 individual 5,000 ft x 5,000 ft tiles and as tiled intensity imagery, and tiled bare earth DEMs formatted to 40 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Breakline data is used to hydroflatten the DEMs created for the PA_WesternPA_2019_D20 Lidar QL2 project. Breaklines are reviewed against LiDAR intensity imagery to verify completeness of capture.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL2; Classified Point Cloud
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| U S Geological Survey |
2020 |
These lidar data are processed Classified LAS 1.4 files, formatted to 2684 individual 10,000 ft x 10,000 ft tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Contours with a 1 foot interval in Esri file geodatabase format.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 2684 individual 10,000 ft x 10,000 ft tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 10,000 ft x 10,000 ft schema. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 22 counties in Pennsylvania, covering approximately 6282 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania North FIPS 3701 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 7229 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs formatted to 1848 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
These are Digital Elevation Model (DEM) data for Pennsylvania as part of the required deliverables for the PA_WesternPA_2019_D20 Lidar QL2 project. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2020 |
Pennsylvania Western Lidar 2020 QL2; Intensity Imagery
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| U S Geological Survey |
2020 |
PA_WesternPA_2019_D20 Lidar QL2 Intensity Imagery.
Geographic Extent: 31 counties in Pennsylvania, covering approximately 9299 total square miles.
Dataset Description: The PA_WesternPA_2019_D20 Lidar QL2 project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 2.1. The data were developed based on a horizontal projection/datum of NAD 1983 2011 StatePlane Pennsylvania South FIPS 3702 Ft US, Foot US and vertical datum of NAVD88 Geoid 12b, Foot US. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 10576 individual 5,000 ft x 5,000 ft tiles, and as tiled intensity imagery and tiled bare earth DEMs 2684 individual 10,000 ft x 10,000 ft tiles. Continuous breaklines were produced in Esri file geodatabase format.
Ground Conditions: LiDAR was collected in fall 2019 and spring 2020, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, NV5 Geospatial, powered by Quantum Spatial utilized a total of 274 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 485 independent accuracy checkpoints, 291 in Bare Earth and Urban landcovers (291 NVA points), 194 in Tall Weeds categories (194 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2017 |
Contours with a 2-foot interval in Esri file shapefile format.
Geographic Extent: 13 counties in Pennsylvania, covering approximately 6,602 total
square miles. Dataset Description: The South Central Pennsylvania 2017 QL2 LiDAR
project called for the planning, acquisition, processing, and derivative products of
lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project
specifications are based on the U.S. Geological Survey National Geospatial Program
Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal
projection/datum of NAD 1983 (2011), UTM Zone 18, meters and vertical datum of NAVD
1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4
files formatted to 7,975 individual 1,500-meter x 1,500-meter tiles, as tiled
intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x
1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase
format. Ground Conditions: LiDAR was collected in fall 2017, while no snow was on
the ground and rivers were at or below normal levels. In order to post process the
LiDAR data to meet task order specifications and meet ASPRS vertical accuracy
guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that
were used to calibrate the LiDAR to known ground locations established throughout
the project area. An additional 245 independent accuracy checkpoints, 142 in Bare
Earth and Urban landcovers (142 NVA points), 103 in Tall Weeds categories (103 VVA
points), were used to assess the vertical accuracy of the data. These checkpoints
were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These lidar data are processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles in NAD83(2011) State Plane Pennsylvania North FIPS 3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data
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| U S Geological Survey |
2019 |
Breakline data is used to hydroflatten the DEMs created for the WVSA, PA 2017 Lidar project project. Breaklines are reviewed against lidar intensity imagery to verify completeness of capture. The compilation procedure included use of lidar intensity, bare earth surface model, point cloud data, and open source imagery in an effort to manually compile hydrologic features in a 2-d environment. Following the compilation phase, a separate process was used to adjust the breakline data to best match the water level at the time of the lidar collection. Any ponds and/or lakes were adjusted to be at or just below the bank and to be at a constant elevation. Any streams were adjusted to be at or just below the bank and to be monotonic. Manual QAQC and peer-based QC review was performed on all delineated data to ensure horizontal placement quality and on all adjusted data to ensure vertical placement quality. Bridge breaklines were also compiled in efforts to generate an accurate DEM product. The final hydrologic and bridge breakline product was delivered in ESRI geodatabase format and was also used in the processing of the DEM deliverableGeographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2019 |
These are Digital Elevation Model (DEM) data for WVSA PA 2017 Impervious Surface Lidar task as part of the required deliverables for WVSA PA 2017 Impervious Surface project. Class 2 (ground) lidar points in conjunction with the hydro breaklines and bridge breaklines were used to create a 1 foot hydro-flattened Raster DEM.Geographic Extent: This task order requires lidar data to be acquired over an AOI surrounding Wilkes-Barre, PA (+/- 401.5 square miles) Dataset Description: WVSA, PA – 2017 Impervious Surface project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83(2011) State Plane Pennsylvania North FIPS3701 Ft US. The vertical datum of NAVD88 Geoid12B Ft US. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files, formatted to 1792 individual 2500 ft x 2500 ft tiles, as tiled Intensity Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected between November 23, 2017 and December 8, 2017 by Woolpert, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established 35 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. Additional independent accuracy checkpoints were collected (35 NVA points and 23 VVA points) and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.
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| U S Geological Survey |
2025 |
Current Land Use
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| Union County |
2025 |
Facility Sites
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| Union County |
2025 |
FEMA Flood Zone
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| Union County |
2025 |
Municipal Boundary
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| Union County |
2025 |
Open Space Conservancy
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| Union County |
2025 |
Tax Parcels
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| Union County |
2025 |
Polling Places
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| Union County |
2025 |
Railroads
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| Union County |
2025 |
Road Centerline
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| Union County |
2025 |
Site Address Point
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| Union County |
2025 |
Voting Precincts
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| Union County |
2025 |
Waterbodies
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| Union County |
2025 |
Waterlines
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| Union County |
2025 |
Zoning District
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| Union County |
2023 |
About GeoPDF data files
PDF (Portable Document Format) digital files are now available for US Geological Survey topographic quadrangle maps. Each file is essentially a scan of a topographic map with the added feature of being georegistered. The files can be used as a PDF file, enabling users to view topo maps onscreen.
The GeoPDF format is an extension to Adobe's PDF 1.3 and higher versions enabling GIS functionality within standard PDF files. This format is designed for the efficient distribution and communication of rich spatial data to anyone who needs to view, review, verify, update, or print it. Because GeoPDF files are highly compressed and encapsulated, they are smaller, faster, and easier to transmit than GIS data sets, without the overhead associated with typical GIS spatial data sets (or the management of database tables, external links, and dependencies). Using the GeoPDF format, publishers of spatial data can select the specific spatial data they want recipients to see and can publish GIS source files into a single GeoPDF file.
GeoPDF files are not a replacement for native GIS formats. GIS professionals still need the original files for editing or updating spatial data. GeoPDF files enable non-GIS professionals, field technicians, business executives, and their colleagues to utilize rich spatial information. Users can view and print GeoPDF files with the free and ubiquitous Adobe Reader ,and they can do more with the data using a free plug-in called TerraGo Toolbar. Users do not have to install this plug-in to view GeoPDF files.
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| USGS Geopdf's for Pennsylvania |
2023 |
Tax Parcels
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| Venango County |
2023 |
Street Centerlines
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| Venango County |
2020 |
Tax Parcels
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| Warren County |
2024 |
Washington County address points generated by the Department of Public Safety
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| Washington County |
2022 |
Washington County amenities maintained by the Washington County GIS Department
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| Washington County |
2023 |
Washington County building outlines generated by the Department of Tax Revenue
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| Washington County |
2024 |
Washington County County Owned Roads
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| Washington County |
2022 |
Washington County election precincts maintained by the Washington County Elections and GIS Departments
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| Washington County |
2022 |
Washington County Magisterial Districts
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| Washington County |
2024 |
Washington County Magistrate Offices
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| Washington County |
2022 |
Washington County municipal outlines generated by the Department of Tax Revenue
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| Washington County |
2022 |
Washington County Municipal Offices
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| Washington County |
2023 |
Parcel data developed by the Department of Tax Revenue
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| Washington County |
2023 |
Washington County parks maintained by the Planning Department
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| Washington County |
2022 |
Washington County polling locations maintained by the Washington County Elections and GIS Departments
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| Washington County |
2024 |
Washington County Road Centerlines generated by the Department of Public Safety
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| Washington County |
2021 |
Washington County School Districts
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| Washington County |
2021 |
Locations of structures detemined via field varification and attributed with latitude and logitude information based on the local project of NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet. Data for structure points and address information is maintained for use in emergency response 911 purposes. This data is created/updated on a daily basis as needed.
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| Wyoming County |
2024 |
Parcel boundaries for Wyoming County, PA
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| Wyoming County |
2024 |
Roads centerline data for Wyoming County, PA. Data has been developed using the basemap of 2012 aerial imagery from County sources. Road name information is determined from local municipal road naming resolutions. Any road numbering information is acquired from PADOT records. Data is updated on a daily basis as creation/updates occur
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| Wyoming County |
2024 |
Locations of all address point locations in York County. Intended for illustration and demonstration purposes. Intended for illustration and demonstration purposes only. Layer was not intended for use under 1:2400
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| York County |
2022 |
Agricultural security areas are intended to promote more permanent and viable farming operations over the long term by strengthening the farming community's sense of security in land use and the right to farm. Agricultural security areas are created by local municipalities in cooperation with individual landowners who agree to collectively place at least 250 acres in an agricultural security area. This is the Ag Security Easements in York County, Pennsylvania. Intended for illustration and demonstration purposes only.
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| York County |
2022 |
Airports and Heliports within York County and those within a 3 mile buffer outside of York County border called "Area of Influence." Intended for illustration and demonstration purposes only.
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| York County |
2022 |
Bike Corridors and Potential Bike Corridors in York County PA
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| York County |
2022 |
Locations of all buildings point locations in York County. The Building Points are broken down into two categories - Main Living Quarters and Auxiliary Building. This data was last updated in 2008-09. Intended for illustration and demonstration purposes only. Layer was not intended for use under 1:2400
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| York County |
2022 |
College Campuses in York County, Pennsylvania. Showing the dormitories and buildings of York County Colleges
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| York County |
2022 |
The agricultural conservation easement may be granted by a farmland owner to the Commonwealth of Pennsylvania, a county agricultural land preservation program, a local government unit or a local land trust. Easements can be sold or donated. After an easement is sold or donated, the conservation easement restrictions are recorded in the recorder of deeds office in the county where the easement is located.
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| York County |
2020 |
York County 2ft Contours. Generated from 2015 LiDAR.
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| York County |
2022 |
York County Boundary Pennsylvania
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| York County |
2022 |
EMS Stations in York County, Pennsylvania
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| York County |
2022 |
Fire Stations in York County, Pennsylvania
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| York County |
2022 |
Police Stations in York County, Pennsylvania
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| York County |
2022 |
Goverment Offices in York County, Pennsylvania
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| York County |
2022 |
Growth Areas as Defined by the York County Planning Commission
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| York County |
2022 |
Impaired streams of York County, Pennsylvania
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| York County |
2022 |
Lakes and ponds of York County, Pennsylvania
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| York County |
2022 |
Land Joins of York County, Pennsylvania
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| York County |
2022 |
2003 York County Landcover Layer. Aerial Imagery used for creation of this dataset was from PAMAP program in 2003. Other Layers Used in this layer is Parcels (for snapping and Landuse codes), Lakes and Ponds, Parks, Roads buffered to 11 feet on each side, Streams buffered to 6 feet on each side and Railroads buffered to 8 feet on each side. Dataset has been dissvoled except for Road Rightaway which has been dissolved by tiles that are used in the PAMAP program. Oldest oblqiue and google streetview was used as well. Intended for illustration and demonstration purposes only
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| York County |
2022 |
Shows Libraries in York County. For illustration purposes only; Not for scales above 1;24000
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| York County |
2022 |
Magisterial Districts in York County, Pennsylvania
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| York County |
2022 |
Existing medical centers in York County, PA
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| York County |
2022 |
Municipal Boundaries in York County, Pennsylvania
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| York County |
2022 |
This shapefile contains the Core Habitat of Biological Diversity Areas identified through the County Natural Heritage Inventory program of the Pennsylvania Natural Heritage Program. County Natural Heritage Inventories focus on areas that are the best examples of ecological resources in a county. Although agricultural lands and open space may be included as part of inventory areas, the emphasis for the designation and delineation of the areas are the ecological values present. Important selection criteria for Natural Heritage Areas are the existence of habitat for plants and animals of special concern, the existence of uncommon or especially important natural communities, and the size and landscape context of a site containing good quality natural features. Large areas and areas that are minimally disturbed by development provide the backbone that links habitats and allows plants and animals to shift and move across sizable portions of the landscape. Core Habitat areas are intended to identify the essential habitat of the species of concern or natural community that can absorb very little activity or disturbance without substantial impact to the natural features. Polygons are based on aerial photo interpretation, field surveys, and existing PNDI data and were delineated by the ecologists on-screen using ArcView (ESRI, Inc., Version 3.3, 8x, and 9x) with the 1:24,000 scale USGS Digital Raster Graphics and/or Digital Aerial Photography images as a background. For each core habitat polygon, the attribute table contains fields indicating the Site Name and Significance. See individual CNHI reports for further information on methodology, site descriptions, and species or communities found at each site.
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| York County |
2022 |
Nursing home locations of York County, Pennsylvania
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| York County |
2022 |
This layer is all the parcels in York County, PA
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| York County |
2022 |
Park and Rides Operated by Rabbit Transit.
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| York County |
2022 |
York County Parks
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| York County |
2022 |
Shows the Subdivsion Data from YCPC
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| York County |
2022 |
A dataset that shows the polling locations of York County Geocoded to the Correct location in the County. Created in cooperation with the Voting Office of York County.
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| York County |
2022 |
York County Railroads. For illustration purposes only; Not for scales above 1;24000
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| York County |
2018 |
York County Existing and Potential Trails
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| York County |
2024 |
This layer is all the Public Roads in York County, PA
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| York County |
2022 |
York County school district boundaries
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| York County |
2022 |
Point locations of all private/public school buildings in York County.
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| York County |
2022 |
Senate District boundaries of York County, Pennsylvania
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| York County |
2014 |
This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.
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| York County |
2022 |
Streams of York County, Pennsylvania
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| York County |
2022 |
Rabbit Transit Routes in the South Central Pa Area
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| York County |
2022 |
Rabbit Transit Bus Stop locations in York County, Pennsylvania
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| York County |
2022 |
Unique features of York County, Pennsylvania
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| York County |
2022 |
Voting Districts in York County, Pennsylvania
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| York County |
2022 |
Zipcodes of York County
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| York County |
2022 |
Shows the Zoning of York County. Updated when Approved by the Planning Commission board and a resolution is passed through the Municipaltiy that is the effected area is.
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| York County |
2022 |
Shows the Zoning overlays of York County. Updated when Approved by the Planning Commission board and a resolution is passed through the Municipaltiy that is the effected area is.
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| York County |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |
2016 |
Multi-spectral digital orthophotography was produced at a scale of 1:2400 (1 in = 200 ft) with a 12 inch pixel resolution for the YCPC project area. Digital orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Orthophoto data is produced through the use of digitized perspective aerial photographs or other remotely sensed image data. This data is processed into a digital product that has been rectified for camera lens distortion, vertical displacement caused by terrain relief, and variations in aircraft altitude and orientation.
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| York County Planning Commission |