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
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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.
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.
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.
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.
Polygons of the buildings footprints clipped Broward County. This is a product MicroSoft.
The orginal dataset This dataset contains 125,192,184 computer generated building footprints in all 50 US states. This data is freely available for download and use.
The data set was clipped to the Broward County developed boundary.
https://github.com/microsoft/USBuildingFootprints/blob/master/README.md">Additional information
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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
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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."
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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.
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This feature class is a compliation GIS dataset that contains building footprints depicting building shape and location in the state of Oregon. All contributing datasets were compiled into the stateside dataset. Static datasets or infrequently maintained datasets were reviewed for quality. New building footprint data were reviewed and digitized from 2017 and 2018 imagery accessed from the Oregon Statewide Imagery Program.
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
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
This chipped training dataset is over Les Cayes 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 1,430 tiles and 28,549 individual buildings. The satellite imagery resolution is 47 cm and was sourced from Maxar ODP (10300100A450A500). Dataset keywords: Urban, Peri-Urban, Rural, Coastal, Mountainous.
Updated building footprint data for Columbia County NY.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 future.Address Point Count: the number of address points that fall within that building footprint.Address 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 GS.Municipality Name: Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GS.Source: 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 tile.Source 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.md
This chipped training dataset is over Paris and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller 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 1,027 tiles and 3,468 buildings. The original dataset was sourced from the SpaceNet 2 Dataset before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.
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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.
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The three-dimensional (3-D) information (i.e., heights) of buildings, in addition to their footprints, is of great importance to a variety of urban studies. This dataset is the first estimated height of each individual building (2020) in the conterminous United States (US) using multi-source remotely sensed observations and the Microsoft open-access building footprint data. The derived building height dataset shows a good agreement with the reference building height data in the conterminous US (i.e., R-square = 0.82, RMSE = 3.30m). This dataset is in shapefile format with building height in attribute tables. The three-dimensional building height dataset reveals spatial variations of urban form at a large scale, deepening our understanding of complex interactions between human society and natural systems.
This chipped training dataset is over Mzuzu 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 3,357 tiles and 91,391 individual buildings. The satellite imagery resolution is 45 cm and was sourced from Maxar ODP (10500100195A6700). Dataset keywords: Urban, Peri-Urban, Dense.
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