The Open Buildings 2.5D Temporal Dataset contains data about building presence, fractional building counts, and building heights at an effective1 spatial resolution of 4m (rasters are provided at 0.5m resolution) at an annual cadence from 2016-2023. It is produced from open-source, low-resolution imagery from the Sentinel-2 collection. The dataset is …

Source: https://sites.research.google/gr/open-buildings/temporal/

The Open Buildings 2.5D Temporal Dataset contains annual data spanning eight years (2016-2023) with building presence, fractional building counts, and building heights covering approximately 58 million square kilometers.

This dataset requires some knowledge with using scripts. The ZIP contains .txt files for over 130 countries and territories. The primary purpose of the data is to support comparison of building footprints across multiple years.

“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.

Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses.

For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the centre of the building. There is no information about the type of building, its street address, or any details other than its geometry.

More information at Google Open Buildings

Google Open Buildings V3 footprint of the country of Chad. This dataset is released to support humanitarian efforts in Chad. For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq The file contains centroids, building footprints (as WKT), and Plus codes.

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.

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

Building footprint polygons are updated weekly by ECGIS. They provide a general reference of where buildings in Eaton County are located. These are not survey-grade.

The Open Database of Buildings (ODB) is a collection of open data on buildings, primarily building footprints, and is made available under the Open Government License - Canada. The ODB brings together 65 datasets originating from various government sources of open data. The database aims to enhance access to a harmonized collection of building footprints across Canada.

Este conjunto de datos abiertos a gran escala consta de esquemas de edificios derivados de imágenes satelitales de alta resolución de 50 cm. Contiene 1,800 millones de detecciones de edificios en África, América Latina, el Caribe, el sur de Asia y el sudeste asiático. La inferencia abarcó un área de 58 millones de km². Para cada edificio de este conjunto de datos, incluimos el polígono que describe…

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 30 m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling. We also identify errors in the original building dataset where buildings are systematically over- or undercounted, providing further guidance for their use in geospatial analysis. This Project is funded by NASA’s Biological Diversity and Ecological Forcasting program; Award # 80NSSC18k0341

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.

A polygon showing the permanent buildings or structures, including the building footprint. Attributes include the building types, building names, building height etc.

Esse conjunto de dados aberto em grande escala consiste em contornos de edifícios derivados de imagens de satélite de alta resolução de 50 cm. Ele contém 1,8 bilhão de detecções de edifícios na África, América Latina, Caribe, Ásia Meridional e Sudeste Asiático. A inferência abrangeu uma área de 58 milhões de km². Para cada edifício nesse conjunto de dados, incluímos o polígono que descreve a área de contato no chão, uma pontuação de confiança que indica a probabilidade de ser um edifício e um Plus Code correspondente ao centro do edifício. Não há informações sobre o tipo de edifício, o endereço ou outros detalhes além da geometria. As pegadas de edifícios são úteis para uma série de aplicações importantes: desde estimativa de população, planejamento urbano e resposta humanitária até ciência ambiental e climática. O projeto é baseado em Gana, com foco inicial no continente africano e novas atualizações sobre o sul da Ásia, o sudeste da Ásia, a América Latina e o Caribe. A inferência foi realizada em maio de 2023. Para mais detalhes, consulte o site oficial do conjunto de dados Open Buildings.

The dataset contains 516M building detections, across an area of 19.4M km2 (64% of the African continent). For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the centre of the building. There is no information about the type of building, its street address, or any details other than its geometry.

The DAS Building Roof Outline layer contains polygon features as a graphical representation for individual building roof edge lines. The layer shows the spatial locations of building roof outlines located throughout the City of Calgary.

To outline the locations of buildings on Parks Canada sites, buildings that Parks Canada manages, and other buildings of interest to Parks Canada. Polygon file to map building footprints of buildings on Parks Canada sites. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor. Data is not necessarily complete - updates will occur weekly.

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.

Cet ensemble de données Open Source à grande échelle se compose de contours de bâtiments dérivés d'images satellite haute résolution de 50 cm. Il contient 1,8 milliard de détections de bâtiments en Afrique, en Amérique latine, dans les Caraïbes, en Asie du Sud et en Asie du Sud-Est. L'inférence a porté sur une superficie de 58 millions de km². Pour chaque bâtiment de cet ensemble de données, nous incluons le polygone décrivant son emprise au sol, un score de confiance indiquant notre degré de certitude qu'il s'agit d'un bâtiment et un Plus Code correspondant au centre du bâtiment. Aucune information n'est disponible sur le type de bâtiment, son adresse ou d'autres détails que sa géométrie. Les empreintes de bâtiments sont utiles pour de nombreuses applications importantes : de l'estimation de la population à la planification urbaine et à l'aide humanitaire, en passant par les sciences de l'environnement et du climat. Le projet est basé au Ghana, avec un accent initial sur le continent africain et de nouvelles informations sur l'Asie du Sud, l'Asie du Sud-Est, l'Amérique latine et les Caraïbes. L'inférence a été effectuée en mai 2023. Pour en savoir plus, consultez le site Web officiel de l'ensemble de données Open Buildings.

City of Salem, Virginia building footprints layer