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

GIS shapefiles of all buildings detected across Antarctica, manually digitised from Google Earth images.

The following provides descriptions of the attributes within the GIS layers:

'STATION' refers to the name of the Research Station or Base

'NAME' refers to a named building within a station (e.g. 'Brookes Hut' which is part of 'DAVIS' within the 'STATION' attributes.

'Ice_free' refers to if a building is located on ice or in an ice-free environment

'0' = a building on ice.

'1' = on an ice-free environment.

'STATUS' refers to the use of the buildings:

1 = Closed site

2 = Lighthouse or camp

3 = Field hut or refuge

4 = Summer/seasonal only

5 = Year round operation.

These data were the output of: Brooks, S. T., Jabour, J., van den Hoff, J. and Bergstrom, D. M. Our footprint on Antarctica competes with nature for rare ice-free land. Nature Sustainability, doi:10.1038/s41893-019-0237-y (2019).

This dataset was last updated on the 30 October 2019 with six additional footprint locations added.

Use Constraints: All use of work must cite use of the data.

This data set conforms to the CCBY Attribution License (http://creativecommons.org/licenses/by/4.0/).

Please follow instructions listed in the citation reference provided at http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=AAS_5134_Antarctic_Disturbance_Footprint when using these data.

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ID: 5028

Metadata ID: AAS_5134_Antarctic_Disturbance_Footprint

UUID: f461a1ca-cc9b-45bb-9a8b-8823aedd9c01

OUTDATED. See the current data at https://data.cityofchicago.org/d/hz9b-7nh8 -- Building footprints in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY.

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.

The 3D Global Building Footprints (3D-GloBFP) dataset is the first global-scale building height dataset at the individual building footprint level for the year 2020, generated through the integration of multisource Earth Observation (EO) data and the extreme gradient boosting (XGBoost) model. The reliability and accuracy of 3D-GloBFP have been validated across 33 subregions, achieving R² values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) between 1.9 m and 14.6 m.

This version supplements building footprints and height attributes for some countries in South America, Asia, Africa, and Europe, based on building footprints provided by Microsoft (https://github.com/microsoft/GlobalMLBuildingFootprints), Open Street Map (https://osmbuildings.org/), Google-Microsoft Open Buildings - combined by VIDA (https://source.coop/repositories/vida/google-microsoft-open-buildings), and EUBUCCO (https://eubucco.com/).

The dataset is divided into spatial grid-based tiles, each stored as an individual ShapeFile (.shp) containing estimated building heights (in meters) in attribute tables. See world_grid.shp and readme.txt for details on the spatial grid and file naming.

Data download links are provided in data_links.txt.

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.

Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.

the BH_100m contains the building height files where each file is named as BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif.

the BF_100m contains the building footprint files where each file is named as BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif.

Here lon_start, lon_end, lat_start, lat_end denote the starting and ending positions of the longitude and latitude of target mapping areas.

To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.

We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:

GC_ROI_def.py defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.

GC_user_download.py retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.

GC_master_pred.py downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.

GC_postprocess.py performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).

GC_postprocess_agg.py aggregates masked patches into larger tiles contained in the GLAMOUR dataset.

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.

OUTDATED. See the current data at https://data.cityofchicago.org/d/hz9b-7nh8 -- Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

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

Building footprints in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY.

“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.

A dataset of building footprints in Morocco, in the area of the 8 September earthquake. Footprint as of May 2023.

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 more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

A dataset of building footprints in Japan, in the area of the January 2024 Noto earthquake. 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 more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

A buildings footprint dataset covering the region of the Herat province which has been hit with multiple earthquake since October 8th 2023. 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 more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

A dataset of building footprints of the Reunion Island, in the area of the January 2023 Belal Cyclone. 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 more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

Building Footprints were digitized from aerial imagery and classified by use type by GIS staff and interns beginning in the Spring of 2014 and updated in December, 2014 and January, 2015. Imagery sources included July, 2011 imagery (ESRI) supplemented by Google Earth Imagery from October, 2014. GIS staff consulted building permits for 2013 and 2014 to ensure recent construction was represented. Assessor data for underlying parcels was used to help determine general use-type for individual structures. However, classification of many smaller utility structures was based partially on aerial photo interpretation. Essential government buildings, utilities and other infrastructure were additionally identified by name and FEMA essential use categories, along with contruction type, in order to facilitate HAZUS disaster damage assessments for the county Emnergency Manager.

Abstract

This is a source dataset created by the Bioregional Assessment Programme without the use of source data.

This dataset contains all of the surface water footprint polygons that were created from mining reports that were used in the surface water modelling. There is also a document with the source references for all of the footprints included in the dataset.

Dataset History

Environmental impact statements and similar documents were downloaded from New South Wales Department of Planning and Environment Major Projects website, and from mining companies' websites. To obtain mine footprints for surface water modelling, the mining reports were searched for past and future projected mine layouts and surface water contributing areas. Each figure was digitised and georeferenced using one of four methods:

1. The preferred method was to use maps or plans with coordinates already on them.

2. If there were no coordinates, then three point locations were matched with points on Google Earth and the latitude and longitude from Google Earth were used to georeference the image.

3. If there were not three clearly identifiable point locations in the image, then supplementary points were found by matching contour information to the Shuttle Radar Topography Mission Smoothed Digital Elevation Model (SRTM DEM-S) grid

Dataset GUID - 12e0731d-96dd-49cc-aa21-ebfd65a3f67a

1. Other site-specific approaches: a. Mangoola Coal Mine did not have adequate georeferencing points in the Year 10 and Final Landform images so these images were georeferenced to the matching project boundary in the other Mangoola Coal Mine images.

b. The West Wallsend Colliery existing pit top surface facilities image, containing a satellite photo background, was georeferenced using Google Earth. The West Wallsend Colliery pit top facility outline was used to georeference the water management system image as they both contained the same outline.

These areas were exported as polygon files (*.poly) using Geosoft Oasis Montaj software.

A list of documents used for creating these polygon files are also included in the dataset

Dataset Citation

Bioregional Assessment Programme (2016) HUN SW footprint shapefiles v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/2a9520c8-1569-4e0e-8bd8-26e2c7b9e9e0.

Abstract The dataset was derived by the Bioregional Assessment Programme from a source dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The Namoi Existing Mine Development Surface Water Footprints represents the area in a mine site that impacts surface runoff through interception preventing its entry to the natural stream network. The footprints includes the entire area disturbed by coal mine operations, including mine pits, roads, spoil dumps, water storages and infrastructure. It may also include otherwise undisturbed parts of the landscape from which natural runoff is drained into mine-disturbed areas or retained in reservoirs. As new parts of the mine lease areas are opened up and rehabilitated areas established, the footprint changes over the life of a mine's operation. This dataset includes the surface water mine footprints data for the Boggabri and Tarrawonga mines in the Namoi subregion. Note: A footprint of the Canyon mine is also included, although this was not used in the modelling. ## Purpose To determine the mine areas affecting surface water runoff for use in the Namoi surface water modelling. ## Dataset History Footprints data for Boggabri, Canyon and Tarrawonga baseline mines were supplied by the NSW Department of Trade and Investment, Regional Infrastructure and Services and have been registered as a separate dataset: Historical Mining Footprints DTIRIS NAM 20150914. The areas covered by these files are the mine pit and support facilities from 2006 to 2012. From this dataset Kmz files have been generated to enable cross-checking of the footprints to images on Google Earth, with modifications made if required. Files with GE in the file name have been digitised from Google Earth. The Canyon mine is already in maintenance and is no longer being actively mined. This mine was not included in the modelling, but is kept here for completeness. ## Dataset Citation Bioregional Assessment Programme (2017) Namoi Existing Mine Development Surface Water Footprints. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/d4875816-4184-4579-8194-8aff6efe7d9a. ## Dataset Ancestors * Derived From Historical Mining Footprints DTIRIS NAM 20150914