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

*** THIS DATA IS A SNAPSHOT AS AT 31ST MARCH 2025 *** A building is defined as basic information about the physical characteristics of the building. A property may consist of a single building or many buildings, associated with one or many holdings.

The ‘Building’ dataset provides key information about the physical characteristics, energy performance and occupation costs of each building. Cost information is only provided for certain types and size of building. Certain buildings may have more than one entry in the data extract as government has more than one ‘interest’ in that property. Again, the extract provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other data extracts.

The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise.

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'.

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.

This dataset pulls from many different data sources to identify individual building characteristics of all buildings in Boston. It also identifies high-potential retrofit options to reduce carbon emissions in multifamily buildings, using the best available data and assumptions from building experts.

Building characteristics will require on-site verification before an owner can act on them.

Find out more about carbon targets for Boston's existing large buildings.

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.

DRAKO specializes in providing Building Footprint Data, offering a unique perspective on spatial analytics and location intelligence. Our data supports businesses in understanding their environments and optimizing their strategies through precise geolocation information.

Building Footprint Data can be used to assess market opportunities, evaluate potential locations, and analyze the competitive landscape. We’re able to provide geographies as addresses, latitude and longitudes; or WKT84 Polygons. Additionally, with our rich dataset, we can provide detailed information about: Business Location, Store Location, and nearby Points of Interest (POI) and Places to ensure you have the insights necessary for informed decision-making. Moreover, we’re also able to reverse geocode data into actionable addresses for you from other geospatial data formats.

Key Features: - Comprehensive mapping of building footprints for accurate spatial analysis Business attributes for each building - Integration with Business Location Data for enhanced market analysis - Access to relevant POIs and Places Data to understand local dynamics - Customizable filters to target specific regions or business types - Able to query by Banner name, ex., Pizza Hut or Walmart - Able to Geofence using foot-print data to create Advertising Audiences using Mobile Location Data

Use Cases: - Site selection and feasibility studies - Market analysis and competitive intelligence - Urban planning and development insights - Real estate assessments and investment strategies - Location-based marketing and outreach

Data Compliance: All of our Building Footprint Data adheres to industry standards for data protection and privacy. We ensure that all data is sourced ethically and responsibly, providing accurate information without compromising user privacy.

Data Quality: DRAKO employs rigorous validation processes to ensure the accuracy and reliability of our Building Footprint Data. Our quality assurance protocols include regular updates and cross-referencing with trusted data sources, ensuring that our information remains current and dependable.

Provides basic information for general acute care hospital buildings such as height, number of stories, the building code used to design the building, and the year it was completed. The data is sorted by counties and cities. Structural Performance Categories (SPC ratings) are also provided. SPC ratings range from 1 to 5 with SPC 1 assigned to buildings that may be at risk of collapse during a strong earthquake and SPC 5 assigned to buildings reasonably capable of providing services to the public following a strong earthquake. Where SPC ratings have not been confirmed by the Department of Health Care Access and Information (HCAI) yet, the rating index is followed by 's'. A URL for the building webpage in HCAI/OSHPD eServices Portal is also provided to view projects related to any building.

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

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

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

Access 3M+ high-precision building footprints across 7 countries, 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.

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

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

This dataset contains building information for all buildings that have completed a WiredNYC survey. This includes buildings that have opted-out from displaying their profiles publicly. Therefore, the building-specific data (e.g. building address) provided is anonymous and only linked to the borough the building is located in.

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.

Seattle’s Building Energy Benchmarking Program (SMC 22.920) requires owners of non-residential and multifamily buildings (Greater than 20,000 square feet) to track energy performance and annually report to the City of Seattle. Annual benchmarking, reporting, and disclosing of building performance are foundational elements of creating more market value for energy efficiency. Per Ordinance (125000), starting with 2015 energy use performance reporting, the City of Seattle is making the data for all buildings greater than 20,000 SF available annually. This dataset contains benchmarking records for all buildings required to report for years 2015-2023. If you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.

This data set contains information integral to the operation for the DOT Headquarters building, to include floor plans, fire control systems, air handling systems, lighting systems, and elevator systems.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.