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

Bing Maps is releasing open building footprints around the world. We have detected 1.3B buildings from Bing Maps imagery between 2014 and 2024 including Maxar, Airbus, and IGN France imagery. The data is freely available for download and use under ODbL.Source: https://github.com/microsoft/GlobalMLBuildingFootprintsFile Geodatabase for download

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.

Over the past few years, Bing Maps has generated high-quality building footprints leveraging AI and harnessing the power of computer vision to identify map features at scale. Applying Deep Neural Networks and ResNet34 to detect building footprints from Bing imagery. Ensuring the best outputs, noise and suspicious data are removed from the predictions.

Morgan County Building footprints generated off of Bing Maps and georefrenced to the 2009 Morgan County Imagery and updated with 2017 NAIP Imagery.

Updated building footprint data for Columbia County NY.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 future.Address Point Count: the number of address points that fall within that building footprint.Address 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 GS.Municipality Name: Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GS.Source: 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 tile.Source 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.md

This map contains Building Footprints of Structures in Waukesha County. Footprint source: Bing Maps

Abstract Built up area polygons represent where buildings are clustered together, such as urban areas. Layer can be used for activities such as monitoring urban grown, or responding to natural disasters. Product has been designed for AUSTopo - Australian Digital Topographic Map Series 250k. Built up area polygons designed for the AUSTopo - Australian Digital Topographic Map Series 250k. Feature class attributes include polygon area (in m2) and feature type (Builtup Area). This dataset provides valuable insights into the built environment of towns and cities, and serves as a crucial resource for urban planners, researchers, policymakers, and developers. Currency Date modified: 31 August 2023 Modification frequency: None Data extent Spatial extent North: -10.15° South: -43.44° East: 153.64° West: 113.42° Temporal extent From 1 January 2013 to 1 January 2018 Source information Catalog entry: Built Up Areas Dataset This dataset is generated from a publicly-available dataset: Bing Building Footprints, using the 'Delineate Built Up Area' tool in ArcGIS Pro. More information on the original source dataset can be found here. Lineage statement Dataset was generated by using the Bing Building Footprints of Australia (October 2020) dataset as an input. Built Up Area layer was created using the Delineate Built Up Areas tool in ArcGIS Pro in April 2023. This layer was produced as part of the update of AUSTopo - Australian Digital Topographic Map Series 250k. This dataset extracted on or before 4 SEPTEMBER 2023. This dataset has been projected from GDA2020 to Web Mercator as part of the Digital Atlas of Austalia project. Minor changes to symbology have been performed only as neccessary to meet the requirements of this project. Data dictionary All layers

Attribute name Description

Object ID Unique identifier for the area polygon

Area (sq. m) Measured area of the built-up region

Feature Type All features in this set are "Builtup Area"

SHAPE_Length Internal - length of the polygon perimeter

SHAPE_Area Internal - area of the generated polygon

Contact Geoscience Australia, clientservices@ga.gov.au

This contains the building damage data described in the manuscript 'A Bayesian Approach for Earthquake Impact Modelling' (available at: https://arxiv.org/abs/2412.15791).The code used to generate the R objects are contained in https://github.com/hamishwp/ODDRIN. It compiles data from several sources including:Global Data Lab: J. Smits and I. Permanyer. The Subnational Human Development Database. Scientific data, 6(1):1–15, 2019.Vs30: D. C. Heath, D. J. Wald, C. B. Worden, E. M. Thompson, and G. M. Smoczyk. A global hybrid VS 30 map with a topographic slope–based default and regional map insets. Earthquake Spectra, 36(3):1570–1584, 2020.Earthquake frequency: K. Johnson, M. Villani, K. Bayliss, C. Brooks, S. Chandrasekhar, T. Chartier, Y. Chen, J. Garcia-Pelaez, R. Gee, R. Styron, A. Rood, M. Simionato, and M. Pagani. Global Earthquake Model (GEM) seismic hazard map (version 2023.1 - June 2023). GEM https://doi.org/10.5281/zenodo.8409647, 2023.Income Inequality: F. Alvaredo, A. B. Atkinson, T. Piketty, and E. Saez. World Inequality Database, 2022. URL http://wid.world/data.Copernicus Building Damage Footprints: Copernicus Emergency Management Service. Copernicus emergency management service - mapping, 2012. URL https://emergency.copernicus.eu/mapping. The European Commission.UNITAR/UNOSAT Building Damage Footprints: UNITAR/UNOSAT. UNITAR’s Operational Satellite Applications Programme – UNOSAT, 2023. URL https://unosat.org/products/.WorldPop Population: A. J. Tatem. WorldPop, open data for spatial demography. Scientific Data, 4(1):1–4, 2017. doi: 10.1038/sdata.2017.4.Bing Building Footprints: Microsoft. Global ML Building Footprints, 2022. URL https://github.com/microsoft/GlobalMLBuildingFootprints. Accessed:2024-06-17.Shakemap: D. J. Wald, B. C. Worden, V. Quitoriano, and K. L. Pankow. ShakeMap manual: Technical manual, user’s guide, and software guide. Technical Report 12-A1, United States Geological Survey, 2005.

This dataset contains high-resolution aerial imagery from the USDA NAIP program [1], high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas. The organization of this dataset (detailed below) will allow users to easily test questions related to this problem of geographic generalization, i.e. how to train machine learning models that can be applied over even wider areas. For example, this dataset can be used to directly estimate how well a model trained on data from Maryland can generalize over the remainder of the Chesapeake Bay.

This layer represents the footprints (area and perimeter) of buildings throughout all of Pend Oreille County, Washington. Great care was taken to map these features with a high degree of accuracy. This data is for reference purposes only.Building footprints for Pend Oreille County were created using a variety of information. A data set of computer-generated building footprints produced by Microsoft Maps served as a starting point for a manual review. The review compared the data set with information from aerial images, Bing Street View, Google Street View, and the Pend Oreille County Assessor’s Office. These sources contained information captured between 2011 and 2021.Throughout the prosses new footprints were added and outdated footprints were removed. Also building footprint categories were designated to each structure based off appearance and context. The categories were:Residence (1) - houses, cabins, yurts, apartment buildings, and multiple family dwellingsManufactured/Mobile (2) - manufactured homes, manufactured structures, trailer homes, and mobile homes (RVs were excluded)Agricultural (3) - greenhouses, stockyard shelters, livestock barns, machinery storage, crop storage, and feed storageShed (4) - garden sheds and equipment shedsPole Building/Utility Building/Garage (5) - out buildings, shops, storage buildings, barns (non-agricultural), pole barns, and kwanzaa hutsCommercial (6) - businesses, stores, lodging, bars, and restaurantsIndustrial (7) - lumber mills, mines, buildings associated with railroads, and buildings associated with power generation.Other (8) - local government buildings, schools, USFS buildings, municipal buildings, churches, public buildings, and unidentified buildings

This feature class was derived from a larger dataset of approximately 2.49 million building footprint polygon geometries in Arizona. These data were downloaded from the GitHub repository at https://github.com/Microsoft/USBuildingFootprints in GeoJSON format. Details on the creation of this dataset is available on GitHub. These data are provided as-is. The building outlines were captured from Bing aerial imagery of various vintages and therefore no statement as to the currency or completeness of these data can be made. Source: Maricopa Association of Governments

Bing Maps ha compartido el open building footprints del mundo. Microsoft detectó 777M de edificaciones para Bing Maps a partir de imágenes comprendidas entre 2014 and 2021 incluyendo fuentes de imágenes de Maxar y Airbus. La data es libre de ser descargada y usada bajo licecia de uso ODbL. Este dataset complemente otros lanzamientos previos.Este feature_class es el resultado de una conversión hecha a partir del formato original GeoJsonl. Los datos están en proyección EPSG:4326 y con encoding UTF-8 tal como están en su fuente. Contiene los registros correspondientes al país Panamá, con un total de 1,127,704 edificaciones.https://github.com/microsoft/GlobalMLBuildingFootprints