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

Microsoft Buildings Footprints with Heights from service: https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/MS_Buildings_Training_Data_with_Heights/FeatureServer (restrictions, do not use)Source: Approx. 9.8 million building footprints for portions of metro areas in 44 US States in Shapefile format.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 CityAlabamaGreater Phoenix City, Mobile, and MontgomeryArizonaTucsonArkansasLittle Rock with 5 buildings just across the river from MemphisCaliforniaBakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to CloverdaleColoradoInterior of DenverConnecticutEnfield and Windsor LocksDelawareDoverFloridaTampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and GainesvilleGeorgiaColumbus, Atlanta, and AugustaIllinoisEast St. Louis, downtown area, Springfield, Champaign and UrbanaIndianaIndianapolis downtown and Jeffersonville downtownIowaDes MoinesKansasTopekaKentuckyLouisville downtown, Covington and NewportLouisianaShreveport, Baton Rouge and center of New OrleansMaineAugusta and PortlandMarylandBaltimoreMassachusettsBoston, South Attleboro, commercial area in Seekonk, and SpringfieldMichiganDowntown DetroitMinnesotaDowntown MinneapolisMississippiBiloxi and GulfportMissouriDowntown St. Louis, Jefferson City and SpringfieldNebraskaLincolnNevadaCarson City, Reno and Los VegasNew HampshireConcordNew JerseyCamden and downtown Jersey CityNew MexicoAlbuquerque and Santa FeNew YorkSyracuse and ManhattanNorth CarolinaGreensboro, Durham, and RaleighNorth DakotaBismarckOhioDowntown Cleveland, downtown Cincinnati, and downtown ColumbusOklahomaDowntown Tulsa and downtown Oklahoma CityOregonPortlandPennsylvaniaDowntown Pittsburgh, Harrisburg, and PhiladelphiaRhode IslandThe greater Providence areaSouth CarolinaGreensville, downtown Augsta, greater Columbia area and greater Charleston areaSouth Dakotagreater Pierre areaTennesseeMemphis and NashvilleTexasLubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus ChristiUtahSalt Lake City downtownVirginiaRichmondWashingtonGreater Seattle area to Tacoma to the south and Marysville to the northWisconsinGreen Bay, downtown Milwaukee and MadisonWyomingCheyenne

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.

125 million building footprints deep learning generated by Microsoft for the USA.

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.

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

The growth of the Wildland-Urban Interface (WUI) underscores the need for accurate mapping to support effective wildfire risk management. One major obstacle is the lack of comprehensive national building footprint databases. Our study addresses that gap by developing a semi-automated, multi-criteria filtering framework aimed at enhancing the quality of global open-source building datasets, with a focus on Microsoft’s Global Building Footprints (MSB), applied to mainland Portugal. The proposed method incorporates regional adaptability and spatial analysis techniques—such as area-based thresholds and proximity criteria—using Portugal’s official Geographic Buildings Location Database (BGE) as a benchmark. To better represent residential structures, the framework systematically removes non-residential anomalies (e.g., industrial complexes, solar farms, transmission lines) through dynamically calibrated thresholds at multiple administrative levels, including municipalities and NUTS-2 regions. As a result, the filtering process reduced the original dataset from approximately 5.6 million to 3.0 million building footprints. The original and filtered datasets are available here.

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.

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.

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

Building Footprints (Microsoft), 20190211 - Shows 3,268,325 building footprints in Indiana. It was produced from data originally created by Microsoft in June 2018 for all 50 U.S. states. Attribute fields showing building footprint perimeter length and area were added (software computed by Esri) by IGWS personnel after the conversion and reprojection of the Microsoft download file named "Indiana.GeoJSON" to an Esri polygon feature class. It was created to provide access to Microsoft's building footprints for Indiana in an Esri GIS file format (file geodatabase).Download Esri File Geodatabase: Building_Footprints_Microsoft.ZIPAccess FGDC metadata: Building_Footprints_Microsoft.HTML or XMLThe following is excerpted from Microsoft's GitHub "USBuildingFootprints" Web page: "Our metrics show that in the vast majority of cases the quality is at least as good as data hand digitized buildings in OpenStreetMap. It is not perfect, particularly in dense urban areas but it is still awesome. The vintage of the footprints depends on the vintage of the underlying imagery. Because Bing Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data. While our metrics show that this data meets or exceeds the quality of hand drawn building footprints, the data does vary in quality from place to place, between rural and urban, mountains and plains, and so on. Inspect quality locally and discuss an import plan with the community."

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.

This dataset is a component of the microsoft building footprint data for all 50 states. There are 380,772 building footprints mapped for the state of Wyoming.

Representative, computer generated building footprints for Rhode Island. Originally developed by Microsoft, these data were released by Microsoft as open source data in June 2018. Source date for these data is unknown, please see metadata for details.Original Microsoft announcement regarding availability of these data.

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 3D Global Building Footprints (3D-GloBFP) dataset is the first global-scale building height dataset at the individual building footprint level for the year 2020, generated through the integration of multisource Earth Observation (EO) data and the extreme gradient boosting (XGBoost) model. The reliability and accuracy of 3D-GloBFP have been validated across 33 subregions, achieving R² values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) between 1.9 m and 14.6 m.

This version supplements building footprints and height attributes for some countries in South America, Asia, Africa, and Europe, based on building footprints provided by Microsoft (https://github.com/microsoft/GlobalMLBuildingFootprints), Open Street Map (https://osmbuildings.org/), Google-Microsoft Open Buildings - combined by VIDA (https://source.coop/repositories/vida/google-microsoft-open-buildings), and EUBUCCO (https://eubucco.com/).

The dataset is divided into spatial grid-based tiles, each stored as an individual ShapeFile (.shp) containing estimated building heights (in meters) in attribute tables. See world_grid.shp and readme.txt for details on the spatial grid and file naming.

Data download links are provided in data_links.txt.

The expansion of the Wildland-Urban Interface (WUI) highlights the critical need for precise mapping to improve wildfire risk management. A key challenge, however, is the scarcity of high-resolution, nationwide building footprint data. To bridge this gap, we developed a semi-automated, multi-criteria filtering framework designed to enhance the quality of open-source global building datasets—specifically Microsoft’s Global Building Footprints (MSB)—for mainland Portugal.

Our methodology combines regional adaptability with spatial analysis techniques, including area-based thresholds and proximity rules, using Portugal’s official Building Geographic Location Database (BGE) as a reference. To optimize residential representation, the framework iteratively removes non-residential outliers (e.g., industrial facilities, solar farms, transmission infrastructure) through dynamically adjusted thresholds applied across administrative levels (municipalities and NUTS-2 regions). As a result, the filtering process reduced the original dataset from approximately 5.6 million to 3.0 million building footprints.

This dataset provides WUI maps for Mainland Portugal, generated using Microsoft’s Global Building Footprints. The geodatabase include WUI maps, original building footprints, and filtered versions for analysis.

Our WUI maps are composed of 11 classes:

Classification of WUI types:

1 - Intermix

2 - Interface

Classification of building density in non-WUI areas:

3 - Very Low

4 - Low

5 - Medium

6 - High

Classification of Land Cover:

200 - Agriculture

300 - Forest

400 - Shrubland

500 - Without Vegetation

600 - Water

This dataset contains computer generated building footprints for Salt Lake City. This data is freely available for download and use.

Building footprint and height data were obtained from the latest global 3D building database. The building footprint data originated from Microsoft and Google datasets. Building height information was estimated using an XGBoost machine learning regression model that integrates multi-source remote sensing features. The height estimation model was trained using datasets from ONEGEO Map, Microsoft, Baidu, and EMU Analytics, utilizing 2020 data for the final estimations. Validation of this database demonstrates that the height estimation models perform exceptionally well at a global scale across both the Northern and Southern Hemispheres. The estimated heights closely match reference height data, especially for landmark buildings, showcasing superior accuracy compared to other global height products. The 3D building data that support this dataset are available in Zenodo DOI:10.5194/essd-16-5357-2024 (Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 3D-GloBFP: the first global three-dimensional building footprint dataset. Earth System Science Data)Based on the 3D building database, we verify the locations and boundaries of individual cultural heritage sites and their buffer zones using UNESCO's heritage map platform (https://whc.unesco.org/), and categorize heritage into three groups for data extraction:Broad Scale Sites: For sites encompassing continuous building clusters or portions of cities (e.g., City of Bath), we extract buildings within the designated buffer zones provided by the UNESCO platform.Single Building Sites: For individual monuments or structures (e.g., Tower of London), we precisely extract the building footprints based on their exact coordinates.Multiple Dispersed Buildings: For sites consisting of multiple, non-contiguous structures (e.g., Wooden Churches of Southern Małopolska, Poland), we identify each location using the platform’s data and verify them through Google Maps before extracting the relevant buildings.A few linear heritage sites, such as extensive archaeological routes spanning over a thousand kilometers, are excluded due to the complexities associated with their vast spatial extent and the variability of climate conditions across different segments.The effective data coverage varies across continents: Europe and North America have an effective rate of 82.5%, Asia and the Pacific 68.3%, Latin America and the Caribbean 75.7%, Arab States 76.5%, and Africa 49.2%. This variability reflects differences in data availability. In less developed regions, remote sensing data tends to overlook non-urban heritage sites, and soil and rock structures common in Africa and Southeast Asia are more difficult to detect using satellite remote sensing techniques, leading to lower effective data coverage in these regions.