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A national dataset of rasterized building footprints for the U.S.

February 27, 2020

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

Citation Information

Publication Year 2020
Title A national dataset of rasterized building footprints for the U.S.
DOI 10.5066/P9J2Y1WG
Authors Mehdi Pourpeikari Heris, Nathan Foks, Kenneth J Bagstad, Austin Troy
Product Type Data Release
Record Source USGS Digital Object Identifier Catalog
USGS Organization Geosciences and Environmental Change Science Center