Building footprints are created from heads up digitizing using 2012/2013 Orthoimagery and 2012 Lidar where Orthoimagery is not available.

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This dataset entails the delineation of impervious surfaces and artificial land cover types extracted from aerial imagery captured in early 2019. Utilization within the City of Austin The dataset plays a pivotal role in several municipal functions, encompassing the computation of the Drainage Charge managed by the Watershed Protection Department, wildfire assessments, emergency operations planning, transportation asset monitoring, urban forest management, and more. Data Updates New aerial imagery and impervious cover data are acquired by the city every two years, resulting in distinct datasets for each capture. As of its initial capture in early 2019, there have been no subsequent updates to this dataset. Downloading Instructions Some users have reported issues downloading the data. Due to the large size of the dataset, downloading can take longer than expected. We recommend following these instructions to download the data.,

This dataset entails the delineation of impervious surfaces and artificial land cover types extracted from aerial imagery captured in early 2021. Utilization within the City of Austin The dataset plays a pivotal role in several municipal functions, encompassing the computation of the Drainage Charge managed by the Watershed Protection Department, wildfire assessments, emergency operations planning, transportation asset monitoring, urban forest management, and more. Data Updates New aerial imagery and impervious cover data are acquired by the city every two years, resulting in distinct datasets for each capture. As of its initial capture in early 2021, there have been no subsequent updates to this dataset. Downloading Instructions Some users have reported issues downloading the data. Due to the large size of the dataset, downloading can take longer than expected. We recommend following these instructions to download the data.,

Note: please go to https://data.sfgov.org/d/ynuv-fyni to access the same data in additional open formats. These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years. The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible. These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall.An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivation https://data.sfgov.org/d/ynuv-fyni/ about

This dataset entails the delineation of impervious surfaces and artificial land cover types extracted from aerial imagery captured in 2023. Utilization within the City of Austin The dataset plays a pivotal role in several municipal functions, encompassing the computation of the Drainage Charge managed by the Watershed Protection Department, wildfire assessments, emergency operations planning, transportation asset monitoring, urban forest management, and more. Data Updates New aerial imagery and impervious cover data are acquired by the city every two years, resulting in distinct datasets for each capture. As of its initial capture in early 2023, there have been no subsequent updates to this dataset. Downloading Instructions Some users have reported issues downloading the data. Due to the large size of the dataset, downloading can take longer than expected. We recommend following these instructions to download the data.,

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

This layer reflects impervious surfaces of the buildings within the Towson University campus boundary. This feature class was converted from a CAD basemap maintained by Towson University Facilities Management.

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

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