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 a ...

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.

Access 4.7M+ high-precision building footprints across the United Kingdom, enabling advanced mapping, location analysis, and strategic decision-making. With 30+ years of data expertise, we provide clean, validated, and enriched datasets to power businesses worldwide.

• Expand market reach with global-scale, high-precision data.
• Enhance mapping, navigation, and spatial analysis.
• Optimize site selection, urban planning, and infrastructure development.
• Improve logistics, delivery routes, and network optimization.
• Assess property values, competitor landscapes, and demographic trends.
• Strengthen disaster management and risk assessment with reliable insights.
• Leverage AI-driven enrichment for deeper, data-driven decision-making.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas:

1. Gain a Competitive Edge with Smarter Mapping: Use building footprint data to analyse competitors, identify high-traffic areas, and optimize locations for maximum market impact.
2. Enhance Navigation & Last-Mile Efficiency: Improve customer experiences with precise building entrances, parking areas, and optimized routes for seamless navigation and delivery.
3. Find the Perfect Site for Growth: Leverage building footprint data to select prime locations, maximize foot traffic, and drive higher sales.
4. Optimize Energy & Infrastructure Planning: Assess rooftop solar potential, utility networks, and energy distribution for smarter, more efficient urban development.
5. Improve Risk Assessment & Security: Use precise building data for insurance underwriting, security planning, and crime prevention strategies.

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.

This polygon feature class depicts buildings throughout Orange County. The object heights and absolute heights are based on 2011 USGS LiDAR data. The height unit is US foot.The values of Address column in "Data" tag are empty for those buildings outside of Orange County.

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

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.

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.

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.

Building Footprints symbolized by Feature Code to match the Community Base Map.Data updated monthly.Data refreshed every 24 hours.

These files contain building outline products for Marin County. The project encompasses the Urban/Suburban land area of Marin County with a 200 feet fringe outside the county boundary. The Digital Terrain Models (DTM) data developed over the Urban/Suburban area mainland covers approximately 210 square miles and over the rural and forest areas covering approximately 525 sq. miles to produce the 100 scale and 400 scale mapping Contours and Ortho Imagery. Builiding footprint outlines cover the same extent as the DTM. Building footprints were produced using stereo pairs from the 2004 orthophoto project to ensure that the on ground bases were captured, and are accurately depicted against a backdrop of the orthophoto sources. Additional footprintswere digitized from 2014 orthophoto as could be seen without tree cover. Many erroneous footprints from 2004 vintage were deleted.

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.

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).

This chipped training dataset is over Karnataka 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 6,288 tiles and 51,335 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (104001002CA32300). Dataset keywords: Rural, Agricultural, Peri-urban.

Shapefile 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

For additional resources, please refer to https://nycmaps-nyc.hub.arcgis.com/search?tags=building&type=feature%2520service%2Cfeature%2520layer

This dataset contains photogrammetrically compiled roof outlines of buildings. All near orthogonal corners are square. Buildings that are less than 400 square feet are not captured. Special consideration is given to garages that are less than 400 square feet and will be digitized when greater than 200 square feet. Interim rooflines, such as dormers and party walls, as well as minor structures, such as carports, decks, patios, stairs, etc., and impermanent structures, such as sheds, are not shown. Large buildings which appear to house activities that are commercial or industrial in nature are shown as commercial/industrial. Structures that appear to be primarily residential in nature, including hotels and apartment buildings are shown as residential buildings. Structures which appear to be used or owned primarily by governmental, nonprofit, religious, or charitable organizations, or which serve a public function are shown as public buildings. Structures which are closely associated with a larger building, such as a garage, are shown as an out building. Structures which cannot be clearly defined as Industrial/Commercial; Residential; Public; or Out Buildings are flagged as such for later categorization. The classification of buildings is subject to the interpretation from the aerial photography and may not reflect the building’s actual use. Buildings that have an area less than the minimum required size for data capture will occasionally be present in the Geodatabase. Buildings are not removed after they have been digitized and determined to be less than the minimum required size. Development Notes: Data meets or exceeds map accuracy standards in effect during the spring of 1992 and updated as a result of a flyover in the spring of 2004 and 2015. Original data was derived from aerial photography flown in the spring of 1992 for the eastern half of the County and the spring of 1993 for the western half of the County. Photography was produced at a scale of 1"=1500'. Mapping was stereo digitized at a scale of 1"=200'.

This chipped training dataset is over Barishal 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 3,024 tiles and 41,248 individual buildings. The satellite imagery resolution is 40 cm and was sourced from Maxar ODP (105001001597B000). Dataset keywords: Urban, Peri-urban, River

The purpose of this dataset is to show the building shape and building locations within Orange and adjacent Counties of photo scale 1:100. New building footprint data was digitized from the imagery captured from June 2020 until December 2020 for buildings larger than 400 sq. ft. The building footprints contain attributes to detail the area, height, elevation and other identifications.This data is a sub-set of the original SCAG data set. It has been trimmed out to be only polygons inside or within 500 feet of the City of Buena Park.