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.

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.

Shapefile of historical footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-planimetrics/blob/master/Capture_Rules.md. Previously posted versions of the data are retained to comply with Local Law 106 of 2015 and can be provided upon request made to Open Data.

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 dataset shows the footprints of all structures within the City of Melbourne. A building footprint is a 2D polygon (or multi-polygon) representation of the base of a building or structure. The footprint is defined as the boundary of the structure where the walls intersect with the ground plane or podium, rather than an outline of the roof area (roofprint).

Where a building has a significant change in built form, multiple footprint polygons are ‘stacked’ vertically to define shape of the built form. This includes, and is not limited to:

• Tower
• Podium
• Setbacks/offsets

The Australian Height Datum (AHD) is the national vertical datum for Australia. The National Mapping Council adopted the AHD in May 1971 as the datum to which all vertical control mapping would be referred

The data was captured in May 2023.

GDB Version: ArcGIS Pro 3.3Additional Resources:Shapefile DownloadREST EndpointBuilding footprints are polygon outlines of structures remotely rendered through digitizing of Virginia Base Mapping Program’s digital ortho-photogrammetry imagery, or digitizing of local government subdivision plats. VBMP building footprints are a collection of locally submitted data and as published from the Virginia Geographic Information Network carry no addressing, nor is there any ownership, resident information, or construction specifications provided. VBMP building footprints are not assumed to be of survey quality and carry no guarantees as to accuracy. Even with these restrictions building outlines are a valuable resource for emergency response operations and for community planning. Currently the Virginia Base Mapping Program’s collection of building footprints consists of over 4 million structures. Data input from localities are processed and published quarterly. To date the majority of Virginia’s localities building footprints have been captured but not all.

Area layers of US, Australia, and Canada building footprints for use with GIS mapping software, databases, and web applications.

Data in this layer is compiled from a variety of sources. Attributes have been added to distinguish the sources."LeePA Building Footprints" are created and maintained by the Lee County Property Appraiser's GIS. The geometry and attributes are extracted from their databases and combined based on the unique building key."LeePA Condo Buildings" are created from features in the Lee County Property Appraiser's parcel fabric. The geometry and attributes are extracted from their databases and combined using a variety of methods.Other buildings have been added by Lee County GIS. These are typically mobile/manufactured homes or time shares. Most mobile/manufactured homes were created using Esri's Building Footprint Extraction deep learning package and Regularize Building Footprint geoprocessing tool from 2024 aerial imagery. Additional attributes were added by Lee County GIS.

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 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.

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

Building Footprints from 2022. Demolished buildings are not included in the web service. Layers from previous years are also included.

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.

(Link to Metadata) This dataset models building footprints in multiple contexts; contexts include emergency management, planning, and analysis. It's based on the VT Building Footprints Geospatial Data Standard.Generally, this dataset is updated weekly.NOTE--This dataset is NOT intended for uses such as property assessment and site engineering.For a dataset that models footprints of other VT E911 features of interest (e.g., solar fields, alpine trails, sporting fields, and quarries/mines), go to VT E911 Other Mapped Features of Interest.

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.

The purpose of this dataset is to show the building shape and building locations within the state of Oregon. The building footprints contain attributes to document source information and for ease of updates. https://ftp.gis.oregon.gov/framework/Preparedness/SBFO_v1.zip

This feature class GIS dataset 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 the Oregon Statewide Imagery Program 2017 and 2018.

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

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).