Data in this layer is compiled from a variety of sources. Attributes have been added to distinguish the sources."LeePA Building Footprints" are created and maintained by the Lee County Property Appraiser's GIS. The geometry and attributes are extracted from their databases and combined based on the unique building key."LeePA Condo Buildings" are created from features in the Lee County Property Appraiser's parcel fabric. The geometry and attributes are extracted from their databases and combined using a variety of methods.Other buildings have been added by Lee County GIS. These are typically mobile/manufactured homes or time shares. Most mobile/manufactured homes were created using Esri's Building Footprint Extraction deep learning package and Regularize Building Footprint geoprocessing tool from 2024 aerial imagery. Additional attributes were added by Lee County GIS.

Area layers of US, Australia, and Canada building footprints for use with GIS mapping software, databases, and web applications.

OUTDATED. See the current data at https://data.cityofchicago.org/d/hz9b-7nh8 -- Building footprints in Chicago. To view or use these files, compression software and special GIS software, such as ESRI ArcGIS, is required. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY.

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

Footprint of buildings (outer envelope) with an area greater than 100 square feet. Interior rooflines, such as dormers, not captured. Attached garages captured as part of the structure. Carports and other extensions over pavement, typically serving as protection from inclement weather, captured as canopies. Decks, patios, stairs, etc. not captured as part of the structure. Countywide data updated Spring 2023. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

This dataset shows the footprints of all structures within the City of Melbourne. A building footprint is a 2D polygon (or multi-polygon) representation of the base of a building or structure. The footprint is defined as the boundary of the structure where the walls intersect with the ground plane or podium, rather than an outline of the roof area (roofprint).

Where a building has a significant change in built form, multiple footprint polygons are ‘stacked’ vertically to define shape of the built form. This includes, and is not limited to:

• Tower
• Podium
• Setbacks/offsets

The Australian Height Datum (AHD) is the national vertical datum for Australia. The National Mapping Council adopted the AHD in May 1971 as the datum to which all vertical control mapping would be referred

The data was captured in May 2023.

GDB Version: ArcGIS Pro 3.3Additional Resources:Shapefile DownloadREST EndpointBuilding footprints are polygon outlines of structures remotely rendered through digitizing of Virginia Base Mapping Program’s digital ortho-photogrammetry imagery, or digitizing of local government subdivision plats. VBMP building footprints are a collection of locally submitted data and as published from the Virginia Geographic Information Network carry no addressing, nor is there any ownership, resident information, or construction specifications provided. VBMP building footprints are not assumed to be of survey quality and carry no guarantees as to accuracy. Even with these restrictions building outlines are a valuable resource for emergency response operations and for community planning. Currently the Virginia Base Mapping Program’s collection of building footprints consists of over 4 million structures. Data input from localities are processed and published quarterly. To date the majority of Virginia’s localities building footprints have been captured but not all.

An in-depth description of the Building Footprint GIS data layer outlining terms of use, update frequency, attribute explanations, and more.

An Esri File Geodatabase containing 2023 footprints for buildings in Cuyahoga County, Ohio.The features were created using orthophotography captured during the spring of 2023. It includes all identified structures with a footprint of at least 100 square feet.Please note that buildings in dense areas (such as Downtown Cleveland) may be combined with neighboring buildings to form one footprint.A hosted feature service containing this data is also available.

To outline the locations of buildings on Parks Canada sites, buildings that Parks Canada manages, and other buildings of interest to Parks Canada. Polygon file to map building footprints of buildings on Parks Canada sites. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor. Data is not necessarily complete - updates will occur weekly.

This data set is a conversion of Califonia building footprint file from GeoJSON format to shapefile format. The California building footprint file which contains 10,988,525 computer generated building footprints in California state is extracting from US building footprint dataset by Microsoft (2018).

The shape and orientation of the ground floor of all structures in a local government. This information is typically compiled from orthoimagery or other aerial photography sources. This representation of the building footprints support the local government basemaps. It also serves as a source for public works, public safety, planning and other agencies that are responsible for the active management of site addresses, facilities, and land use information.

Tarrant County Building Footprints. Computer generated building footprints for the United States. The original dataset contains 125,192,184 computer generated building footprints in all 50 US states. This data is freely available for download and use. The original dataset has been pared down to include only Tarrant County building footprints. The filter extent used also includes a portion of other counties that surround Tarrant County. License: This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL) FAQ: What the data include: Approximately 125 million building footprint polygon geometries in all 50 US States in GeoJSON format. Source: https://github.com/Microsoft/USBuildingFootprints

** Download as File GeodatabaseThe Countywide building outline dataset contains building outlines (over 3,000,000) for all buildings in Los Angeles County, including building height, building area, and the parcel number (also known as building footprints). This data was captured from stereo imagery as part of the LARIAC2 Project (2008 acquisition) and was updated as part of the LARIAC4 (2014) imagery acquisition. There are a number of sources. All buildings were updated to include changes between 2008 and 2014.

City of Palmdale - building outlines from the LARIAC (2006) imagery derived from orthogonal imagery. City of Pasadena - building outlines from earlier imagery, updated with LARIAC2 (2008) imagery in 2008 City of Glendale - building outlines from earlier imagery, updated with LARIAC2 (2008) imagery in 2008 City of Los Angeles - building outlines from LARIAC2 (2008), stereo generated, for all buildings > 64 square feet The rest of the County - building outlines from LARIAC2 (2008), stereo generated, for all buildings > 400 square feet Most of the buildings in this dataset were generated using stereo imagery. This means that the person capturing the buildings actually saw them in 3-D, and therefore was able to more accurately capture the location of the roof line, since this method eliminated the impacts of building lean (where the height of the building impacts its apparent location). Basically this is the most accurate method for capturing building outlines. In many cases the location is more accurate than our aerial photography and parcel boundaries.This file contains a file geodatabase which has two feature classes:

LARIAC4_BUILDINGS_2014 – this is the current set of buildings as of 2014 LARIAC2_BUILDINGS_DELETED_2014 – these are the buildings from LARIAC2 that have been modified or deleted. These can be for change analysis and detection.

Note: please go to https://data.sfgov.org/d/ynuv-fyni to access the same data in additional open formats.

These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years. The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible. These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall.An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivation https://data.sfgov.org/d/ynuv-fyni/ about

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

All buildings over 64 square feet in City of Los Angeles captured through LARIAC4 4" and 1' imagery. LARIAC4 guide: https://lariac-lacounty.hub.arcgis.com/pages/lariac4-documents-dataCountywide Building Outlines download available from LA County at: https://data.lacounty.gov/maps/57f5fc977d6a427a978003a6229ab5e7/aboutData is from 2014.

These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years.The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible.These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall.

An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivation https://data.sfgov.org/d/72ai-zege/about

To get this data as a File Geodatabase, go to https://data.sfgov.org/d/asx6-3trm

description: Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.; abstract: Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

GRANT OF LICENSE. Subject to the Distributor’s compliance with the End User License Agreement in Schedule B, and the Agreement, Ecopia grants Distributor a non-exclusive, non- transferrable license to distribute the Product to End User. Distributor will distribute the Products to the End User under the following terms:(a) Type. Internal Use License(b) Term. Perpetual(c) End Users. The “End User” for the purposes of the End User License Agreement ofSchedule B shall be interpreted as: Local, and State government organizations of the State Alaska, and Federal government organizationsEcopia will maintain ownership and all associated right, title, and interest in the Products and of all technology used for the generation of the Products. Conditional upon End User’s compliance with these License Terms and the applicable Single Distribution Agreement, during the Term, Ecopia grants to End User a non-exclusive, non- transferable, limited license, to allow an unlimited number of its Authorized Users to:(a) store, access, evaluate, reproduce, and use the Product solely for End User’s Internal Use;(b) create Derivatives of the Product, and store, evaluate, reproduce, and use those Derivatives, all solely for End User’s Internal Use;(c) display Derivatives on a public-facing platform, in a view only, non-downloadable format; and(d) submit point-based challenges, in example, the address may be included, but not the building footprint coordinates.Customer is responsible for ensuring that its Authorized Users comply with these License Terms, and Customer is liable for the acts and omissions of its Authorized Users.DERIVATIVES. A derivative of the Product is any addition, improvement, update, modification, transformation, adaptation, or derivative work of or to the Product, including, for example, any addition or extraction of data or content to or from the Product. Distributor may create a derivative of the Product and provide such derivative solely to the End User. Distributor shall not and shall not permit any third party, except the End User, to access or use the Product or any derivatives. For the sake of clarity, Distributor is expressly forbidden from using the Product, or any derivative derived from the Product, for the purpose of supporting multiple end users.