The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values a ...

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.

Bing Maps is releasing open building footprints around the world. We have detected 1.3B buildings from Bing Maps imagery between 2014 and 2024 including Maxar, Airbus, and IGN France imagery. The data is freely available for download and use under ODbL.Source: https://github.com/microsoft/GlobalMLBuildingFootprintsFile Geodatabase for download

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling. This Project is funded by NASA’s Biological Diversity and Ecological Forcasting program; Award # 80NSSC18k0341

From: MS BuildingsMicrosoft recently released a free set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

Alabama Greater Phoenix City, Mobile, and Montgomery

Arizona Tucson

Arkansas Little Rock with 5 buildings just across the river from Memphis

California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

Colorado Interior of Denver

Connecticut Enfield and Windsor Locks

Delaware Dover

Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

Georgia Columbus, Atlanta, and Augusta

Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

Indiana Indianapolis downtown and Jeffersonville downtown

Iowa Des Moines

Kansas Topeka

Kentucky Louisville downtown, Covington and Newport

Louisiana Shreveport, Baton Rouge and center of New Orleans

Maine Augusta and Portland

Maryland Baltimore

Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

Michigan Downtown Detroit

Minnesota Downtown Minneapolis

Mississippi Biloxi and Gulfport

Missouri Downtown St. Louis, Jefferson City and Springfield

Nebraska Lincoln

Nevada Carson City, Reno and Los Vegas

New Hampshire Concord

New Jersey Camden and downtown Jersey City

New Mexico Albuquerque and Santa Fe

New York Syracuse and Manhattan

North Carolina Greensboro, Durham, and Raleigh

North Dakota Bismarck

Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

Oklahoma Downtown Tulsa and downtown Oklahoma City

Oregon Portland

Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

Rhode Island The greater Providence area

South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

South Dakota greater Pierre area

Tennessee Memphis and Nashville

Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

Utah Salt Lake City downtown

Virginia Richmond

Washington Greater Seattle area to Tacoma to the south and Marysville to the north

Wisconsin Green Bay, downtown Milwaukee and Madison

Wyoming Cheyenne

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.

This feature layer is Microsoft's recently released, free, set of deep learning generated building footprints covering the United States of America. In support of this great work and to make these building footprints available to the ArcGIS community, Esri has consolidated the buildings into a single layer and shared them in ArcGIS Online. The footprints can be used for visualization using vector tile format or as hosted feature layer to do analysis. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.The original AGOL Item was produced by ESRI and is located here.

Computer generated building footprints for the Tennessee. Comes out of the open source project by Microsoft to map all the buildings in the USA. More details can be found at https://github.com/Microsoft/USBuildingFootprints

This vector dataset contains information about individual building footprints covering all countries of the European Union (EU27). This is the result of conflating the building footprint polygons available in three datasets, and in the following order of priority: OpenStreetMap, Microsoft GlobalML Building Footprints and European Settlement Map.

Results indicate how DBSM R2023 compares robustly agains cadastral data from Estonia, used as reference area.

The comparison with GHS-BUILT-S, reveals a relative overestimation of the latter, factored by 0.68 at the EU scale for a sound match. While this dataset only contains the polygon of the building footprint, the aim is to continue to add relevant attributes from the point of view of energy efficiency and energy consumption in building in future versions.

Graph and download economic data for New Private Housing Units Authorized by Building Permits for Mississippi (MSBPPRIVSA) from Jan 1988 to May 2025 about permits, MS, buildings, new, private, housing, and USA.

Computer generated buiilding footprints for Maryland. The methodology for the generation of the building footprints can be found at: https://github.com/Microsoft/USBuildingFootprints. These building footprints should be used a reference only and the geometries are not considered accurate enough to provide detailed estimates related to their location, area, or associated attributes.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Map Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_BuildingFootprints/MapServer

Graph and download economic data for New Private Housing Structures Authorized by Building Permits for Amite County, MS (BPPRIV028005) from 1990 to 2024 about Amite County, MS; permits; MS; buildings; private; housing; and USA.

This chipped training dataset is over Mesopotamia and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,013 tiles and 33,139 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (10500100236CC900). Dataset keywords: Coastal, Urban, Peri-urban.

This chipped training dataset is over Wa and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 7,615 tiles and 68,072 individual buildings. The satellite imagery resolution is 32 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-urban

Mississippi building outline dataset.

Comprehensive dataset of 1,352 Apartment buildings in Mississippi, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.