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Filters: Tags: Building Footprint (X) > partyWithName: U.S. Geological Survey - ScienceBase (X)

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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...
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Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available...
    map background search result map search result map A national dataset of rasterized building footprints for the U.S. Building locations identified before and after the Camp, Tubbs, and Woolsey wildfires Building locations identified before and after the Camp, Tubbs, and Woolsey wildfires A national dataset of rasterized building footprints for the U.S.