The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".

1. Data, separated by state - minimalist list of each building (rows) for the following fields (columns)
   1. ID - unique building ID
   2. Footprint2D - lat/lon vertices of building footprint
   3. State_Abbrev - Abbreviation for the from which building is located
   4. Area - estimate of total conditioned floor area (ft2)
   5. Area2D - footprint area (ft2)
   6. CZ - ASHRAE Climate Zone designation
   7. Height - building height (ft)
   8. NumFloors - number of floors (above-grade)
   9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)
   10. CZ - US climate zone designation
   11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards
   12. Standard - building vintage (determined by building age)

This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

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.

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.

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.The original source for these layers are: Business Data: InfoUSA business database purchased by DDP in 2017Building Data: Detroit Building Footprint data Parcel Data: from Detroit Open Data Portal, download in May 2018.For field research by Tian, some fields have been added and some records in building and business have been edited. For business data, Tian confirmed most of public assessable businesses and deleted those which do not exist. Also, Tian add new Business to the business data if it did not exist on the record. For building data, Tian recorded the total business space for each building, not-empty business space, occupancy status, parking adjacency status, and took picture for every building in downtown Detroit. Detail field META DATA:InfoUSA Business OBJECTID_1 COMPANY_NA: company nameADDRESS: company addressCITY: citySTATE: stateZIP_CODE: zip codeMAILING_CA: source InfoUSAMAILING_DE source InfoUSALOCATION_A source InfoUSA: addressLOCATION_1 source InfoUSA: cityLOCATION_2 source InfoUSA: stateLOCATION_3 source InfoUSA: zip codeLOCATION_4source InfoUSALOCATION_5 source InfoUSACOUNTY: countyPHONE_NUMB: phone numberWEB_ADDRES: website addressLAST_NAME: contact last nameFIRST_NAME: contact first nameCONTACT_TI: contact type CONTACT_PR: CONTACT_GE: contact genderACTUAL_EMP: employee numberEMPLOYEE_S: employee number classACTUAL_SAL: actual sale SALES_VOLU: sales value PRIMARY_SI: primary sales valuePRIMARY_1: primary classificationSECONDARY_: secondary classification SECONDARY1SECONDAR_1SECONDAR_2CREDIT_ALP: credit level CREDIT_NUM: credit numberHEADQUARTE: headquarteYEAR_1ST_A: year openOFFICE_SIZ: office sizeSQUARE_FOO: square footFIRM_INDIV:PUBLIC_PRI Fleet_size FRANCHISE_ FRANCHISE1 INDUSTRY_SADSIZE_IN_METRO_AREA INFOUSA_ID LATITUDE: yLONGITUDE: xPARKING: parking adjacency NAICS_CODE: NAICS CODENAICS_DESC: NAICS DESCRIPTION parcelnum*: PARCEL NUMBER parcelobji* PARCEL OBJECT IDCHECK_* ACCESSIABLE* PUBLIC ACCESSIBILITYPROPMANAGER* PROPERTY MANAGERGlobalID Notes: field with * means it came from other source or field research done by Tian Xie in Aug, 2018BuildingOBJECTID_12 BUILDING_I: building idPARCEL_ID : parcel id BUILD_TYPE: building type CITY_ID：city id APN: parcel number RES_SQFT: Res square feet NONRES_SQF non-res square feetYEAR_BUILT: year built YEAR_DEMOHOUSING_UN: housing unitsSTORIES: # of stories MEDIAN_HGT: median height CONDITION: building condition HAS_CONDOS: has condos or not FLAG_SQFT: flag square feet FLAG_YEAR_: flag yearFLAG_CONDI: flag condition LOADD1: address number HIADD1 (type: esriFieldTypeInteger, alias: HIADD1, SQL Type: sqlTypeOther, nullable: true, editable: true)STREET1: street name LOADD2: HIADD2 (type: esriFieldTypeString, alias: HIADD2, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)STREET2 (type: esriFieldTypeString, alias: STREET2, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)ZIPCODE: zip code AKA: building name USE_LOCATOTEMP (type: esriFieldTypeString, alias: TEMP, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)SPID (type: esriFieldTypeInteger, alias: SPID, SQL Type: sqlTypeOther, nullable: true, editable: true)Zone (type: esriFieldTypeString, alias: Zone, SQL Type: sqlTypeOther, length: 60, nullable: true, editable: true)F7_2SqMile (type: esriFieldTypeString, alias: F7_2SqMile, SQL Type: sqlTypeOther, length: 10, nullable: true, editable: true)Shape_Leng (type: esriFieldTypeDouble, alias: Shape_Leng, SQL Type: sqlTypeOther, nullable: true, editable: true)PARKING*: parking adjacency OCCUPANCY*: occupied or not BuildingType* : building type TotalBusinessSpace*: available business space in this buildingNonEmptySpace*: non-empty business space in this buildingCHECK_* FOLLOWUP*: need followup or notGlobalID*PropmMana*: property manager Notes: field with * means it came from other source or field research done by Tian Xie in Aug, 2018

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.

Benefits and key featuresUnderstand 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.Present accurate information consistently with other available open data products.

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.

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.

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.

“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.

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 statistic shows the amount of data collected by smart buildings worldwide, from 2010 to 2020. In 2015, smart buildings collected 7.8 zetabytes of data globally, through a range of sensors and smart and connected devices.

(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.

Access 114M+ high-precision building footprints across 220 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.

This dataset contains the list of NYC (New York City) Properties under DOB (Department of Buildings) jurisdiction.

The shape and orientation of the ground floor of all structures in a local government. This information is typically compiled from orthoimagery or other aerial photography sources. This representation of the building footprints support the local government basemaps. It also serves as a source for public works, public safety, planning and other agencies that are responsible for the active management of site addresses, facilities, and land use information.

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

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) – OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). “Model America - data and models of every U.S. building. [Data set].” Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

Echo’s Building Footprint datasets have been built to ensure ongoing accuracy & precision.

We use satellite imagery, machine learning & human verification, to define the most precise boundaries of any location (stores, landmarks, amenities, offices, etc.) and build polygon-based "geofences" around 25.4M+ places in the US, U.K., France, Spain, Italy and Germany.

By doing so, it is possible to identify the precise footprints so companies can benefit from an accurate geospatial analysis of countries, cities, or even local neighbourhoods.

With Echo's Building Footprint data it is possible to: - Build location-based strategies fast - Increase accuracy of Foot Traffic data - Pinpoint locations for highly precise Mapping - Improve Site Selection processes

Clients using this dataset are commonly from: - Commercial Real Estate & PropTech - Retail - Investment Banking