Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development. They also have use in insurance, taxation, change detection, infrastructure planning, and a variety of other applications.

Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models are highly capable of learning these complex semantics and can produce superior results. Use this deep learning model to automate the tedious manual process of extracting building footprints, reducing time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work well in the United States.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.718.Sample resultsHere are a few results from the model. To view more, see this story.

This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work in Africa and gives the best results in Uganda and Tanzania.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.786.Sample resultsHere are a few results from the model. To view more, see this story.

Walk through this tutorial to get started with using the building footprint extraction deep learning model in ArcGIS Pro.

This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery. Note: Imagery has to be analyzed at 30 cm resolution for best results.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work in Australia.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 79.4 percent.Training dataThis model has been trained on an Esri proprietary building footprint extraction dataset.Limitations • False positives are observed near the costal areas. These can be filtered out using the confidence values. • A random shift between footprints and imagery (around 3-7 meter) has been observed in some areas. • The model does not work well with highly oblique (off nadir) imagery, especially when delineating footprints of high rise buildings.Sample resultsHere are a few results from the model. To view more, see this story.

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.

Buildings are the foundation of any 3D city; they create a realistic visual context for understanding the built environment. This rule can help you quickly create 3D buildings using your existing 2D building footprint polygons. Create buildings for your whole city or specific areas of interest. Use the buildings for context surrounding higher-detail buildings or proposed future developments.Already have existing 3D buildings? Check out the Textured Buildings from Mass by Building Type rule.What you getA Rule Package file named Building_FromFootprint_Textured_ByLandUse.rpk Rule works with a polygon layerGet startedIn ArcGIS Pro Use this rule to create Procedural Symbols, which are 3D symbols drawn on 2D features Create 3D objects (Multipatch layer) for sharing on the webShare on the web via a Scene LayerIn CityEngine:CityEngine File Navigator HelpParametersBuilding Type: Eave_Height: Height from the ground to the eave, units controlled by the Units parameterFloor_Height: Height of each floor, units controlled by the Units parameterLand_Use: Use on the land and type of building, this helps in assigning appropriate building texturesRoof_Form: Style of the building roof (Gable, Hip, Flat, Green)Roof_Height: Height from the eave to the top of the roof, units controlled by the Units parameterDisplay:Color_Override: Setting this to True will allow you to define a specific color using the Override_Color parameter, and will disable photo-texturing.Override_Color: Allows you to specify a building color using the color palette. Note: you must change the Color_Override parameter from False to True for this parameter to take effect.Transparency: Sets the amount of transparency of the feature Units:Units: Controls the measurement units in the rule: Meters | FeetNote: You can hook up the rule parameters to attributes in your data by clicking on the database icon to the right of each rule parameter. The database icon will change to blue when the rule parameter is mapped to an attribute field. The rule will automatically connect when field names match rule parameter names. Use layer files to preserve rule configurations unique to your data.For those who want to know moreThis rule is part of a the 3D Rule Library available in the Living Atlas. Discover more 3D rules to help you perform your work.Learn more about ArcGIS Pro in the Getting to Know ArcGIS Pro lesson

Polygon geometry displaying Building Footprints in East Baton Rouge Parish, Louisiana.Metadata

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.

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.

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.

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://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_BuildingFootprints/MapServer

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.

A 3D multi-patch feature service of building footprints in San Bernardino County's Primary Urban Area as of 2021. Data was created as an ancillary product of aerial imagery.

This dataset contains polygons representing the footprint of structures within Indiana. This data was derived by the Polis Center at Indiana University Purdue University Indianapolis (IUPUI) from statewide LiDAR data collected during the 2016-2020 USGS 3DEP program. This dataset was developed as part of the Cooperating Technical Partner program with the Federal Emergency Management Agency and the Indiana Department of Natural Resources.

Building. The dataset contains polygons representing planimetric buildings, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO). These features were originally captured in 1999 and updated in 2005, 2008, and 2010. The following planimetric layers were updated: - Building Polygons (BldgPly) - Bridge and Tunnel Polygons (BrgTunPly) - Horizontal and Vertical Control Points (GeoControlPt) - Obscured Area Polygons (ObsAreaPly) - Railroad Lines (RailRdLn) - Road, Parking, and Driveway Polygons (RoadPly) - Sidewalk Polygons (SidewalkPly) - Wooded Areas (WoodPly) Two new layers were added: - Basketball and Other Recreation Courts (RecCourtPly) - Wheelchair Ramps (TransMiscPt).

This data set is a collection of polygons representing the roof line of built structures wholly or partially within the State of North Carolina political boundary. The building footprints are closed polygons with a unique identifier and have the square footage calculated. The polygons were not required to be rectilinear (i.e. interior angles = 90 degrees), but they should give an accurate representation of the building when viewed at a scale of 1:1500 in ArcGIS.

This data shows the digitized building footprints of buildings located within the City of Winchester, Virginia. This data was collected off Eagleview 2017 aerial imagery and was provided to the City after the flight.

Buildings throughout the City of Ottawa Accuracy: Buildings collected from aerial photographs at a scale of 1:10,000 Update Frequency: Data collected in 2014. Update frequency currently unknown. Contact: Survey and Mapping

These building structure centroids are a merge of two different sources 1) extracted from the NGA Building Footprints (LIDAR) dataset of 2005 and 2) from USI Hawaii deliverables. Unique ids were added throughout (SOI). This dataset is maintained by DPP on an ongoing basis.

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.