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

Building structures include parking garages, ruins, monuments, and buildings under construction along with residential, commercial, industrial, apartment, townhouses, duplexes, etc. Buildings equal to or larger than 9.29 square meters (100 square feet) are captured. Buildings are delineated around the roof line showing the building "footprint." Roof breaks and rooflines, such as between individual residences in row houses or separate spaces in office structures, are captured to partition building footprints. This includes capturing all sheds, garages, or other non-addressable buildings over 100 square feet throughout the city. Atriums, courtyards, and other “holes” in buildings created as part of demarcating the building outline are not part of the building capture. This includes construction trailers greater than 100 square feet. Memorials are delineated around a roof line showing the building "footprint."Bleachers are delineated around the base of connected sets of bleachers. Parking Garages are delineated at the perimeter of the parking garage including ramps. Parking garages sharing a common boundary with linear features must have the common segment captured once. A parking garage is only attributed as such if there is rooftop parking. Not all rooftop parking is a parking garage, however. There are structures that only have rooftop parking but serve as a business. Those are captured as buildings. Fountains are delineated around the base of fountain structures.

Landmarks and Government BuildingsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays Cemeteries, Post Offices, City/Town Halls, Courthouses, State Capitols, State Supreme Courts, The White House, U.S. Capitol, U.S. Supreme Court, Historic Sites/Points of Interest, and National Symbols/Monuments in the U.S. Per the USGS, "Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations."Supreme Court of WyomingData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Landmarks & Government Buildings) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 135 (USGS National Structures Dataset - USGS National Map Downloadable Data Collection)OGC API Features Link: (Landmark Structures - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: The National MapFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

Important Note: This item is in mature support as of December 2024. See blog for more information.This web scene features OpenStreetMap (OSM) 3D buildings and trees layers hosted by Esri. Esri created the 3D scene layers of buildings and trees from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting the layers in this scene are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset is a categorical mapping of estimated mean building heights, by Census block group, in shapefile format for the conterminous United States. The data were derived from the NASA Shuttle Radar Topography Mission, which collected “first return” (top of canopy and buildings) radar data at 30-m resolution in February, 2000 aboard the Space Shuttle Endeavor. These data were processed here to estimate building heights nationally, and then aggregated to block group boundaries. The block groups were then categorized into six classes, ranging from “Low” to “Very High”, based on the mean and standard deviation breakpoints of the data. The data were evaluated in several ways, to include comparing them to a reference dataset of 85,000 buildings for the city of San Francisco for accuracy assessment and to provide contextual definitions for the categories.

Ordnance Survey ® OpenMap - Local Buildings are polygon features that represent a built entity that includes a roof. This is a generalized building and could be made up of an amalgamation of other buildings and structures.Ordnance Survey ® OpenMap - Local Important Buildings are polygon features that represent buildings that fall within the extent of a functional site across England, Wales and Scotland. Important Buildings are classified into a number of building themes such as:Attraction and Leisure - A feature that provides non-sporting leisure activities for the public. Includes Tourist Attractions.Air Transport - This theme includes all sites associated with movement of passengers and goods by air, or where aircraft take off and land. Includes Airport, Helicopter Station, Heliport.Cultural Facility - A feature that is deemed to be of particular interest to society. Includes Museum, Library, Art Gallery.Education facility - This theme includes a very broad group of sites with a common high level primary function of providing education (either state funded or by fees). Includes: Primary Education, Secondary Education, Higher or University Education, Further Education, Non State Secondary Education, Non State Primary Education, Special Needs Education.Emergency Services - Emergency services are organizations which ensure public safety and health by addressing different emergencies. Includes: Fire Station, Police Station.Medical Facility - This theme includes sites which focus on the provision of secondary medical care services. Includes: Medical Care Accommodation, Hospital, Hospice.Religious Building - A place where members of a religious group congregate for worship. Includes: Places of Worship (churches etc.)Retail - A feature that sells to the general public finished goods. Includes: Post OfficeRoad Transport - This theme includes: Bus Stations, Coach Stations, Road user services.Sports and Leisure Facility - A feature where many different sports can be played. Includes: Sports and Leisure CentreWater Transport - This theme includes sites involved in the transfer of passengers and or goods onto vessels for transport across water. Includes: Port consisting of Docks and Nautical Berthing, Vehicular Ferry Terminal, Passenger Ferry Terminal.With OS OpenMap - Local Buildings and Important Buildings you can:Understand your area in detail, including the location of key sites such as schools and hospitals.Share high-quality maps of development proposals to help interested parties to understand their extent and impact.Analyse data in relation to important public buildings, roads, railways, lines and more.Use in conjunction with other layers such as Functional Sites – an area or extent which represents a certain type of function or activity.Present accurate information consistently with other available open data products.The currency of the data is 04/2025

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.

Download In State Plane Projection Here. The pavement boundaries were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from photography taken between March 15 and April 25, 2018. This dataset should meet National Map Accuracy Standards for a 1:1200 product. Lake County staff reviewed this dataset to ensure completeness and correct classification. In the case of a divided highway, the pavement on each side is captured separately. Island features in cul-de-sacs and in roads are included as a separate polygon.These building outlines were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from successive years of photography. The most recent aerial photography was flown between March 11 and April 12, 2017. This dataset should meet National Map Accuracy Standards for a 1:1200 product. All the enclosed structures in Lake County with an area larger than 100 square feet as of April 2014 should be represented in this coverage. It should also be noted that a single polygon in this dataset could be composed of many structures that share walls or are otherwise touching. For example, a shopping mall may be captured as one polygon. Note that the roof area boundary is often not identical to the building footprint at ground level. Contributors to this dataset include: Municipal GIS Partners, Inc., Village of Gurnee, Village of Vernon Hills.

November 2024

This dataset is built from the Overture 2023-07-26-alpha.0 version of open map data by the Overture Maps Foundation. This dataset compiles building footprints and their attributes for individual cities for convenient and lightweight spatial analytics.Credits: Overture Maps FoundationLicense: https://opendatacommons.org/licenses/odbl/

Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.

This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.

Temporal extent The acquisition dates of the different data sources vary to some degree: - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017. - Dependent variables: the 3D building models are from 2012-2020 depending on data provider. - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016. Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.

Further information For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This Web Map is a subset of Open Street Maps Buildings for Asia Feature Layer to focus on Central Asia and Caucasus Region. If you would like to access the data please use the feature layer that provides access to OpenStreetMap. This feature layer provides access to OpenStreetMap (OSM) buildings data for Asia, which is updated every 1 minute with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM polygon (closed way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes building features defined as a query against the hosted feature layer (i.e. building is not blank).In OSM, a building is a man-made structure with a roof, standing more or less permanently in one place. These features are identified with a building tag. There are thousands of different tag values for building used in the OSM database. In this feature layer, unique symbols are used for several of the most popular building types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Streets level or 1:10k scale) to see the building features display. You can click on a feature to get the name of the building (if available). The name of the building will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this buildings layer displaying just one or two building types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. building is apartments), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. parks) that are ready to use, but not for every type of building.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

This layer represents the building footprint geometry in the City of Bloomington. These footprints are derived from 2021 aerial imagery and should be considered an approximation of the buildings dimensions.

This dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display all building outlines as an area (polygon) within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

This map data layer represents the building footprints for the City of Cupertino, California. The mapped geographic area includes 11.3 square miles of western Santa Clara County in California. The building footprints data layer was originally based on aerial photographs from 2011. Continual updates are made as needed. Most updates come from digitized plat/plan approvals or from completed City project plans. Mapping accuracy meets National Map Accuracy Standards for +/-2.5 US feet. Spatial coordinate system is California State Plane West, zone III Fipszone 0403 Adszone 3326, NAD83. Scale of true display is 1:1200 (100' scale).

LOD1 building data used for noise mapping.

Buildings: A simplified point layer of California State Parks buildings, providing location, name, function and other attributes. Current as of October 2024.

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.