A rasterized building footprint dataset for the United States

Scientific Data
By: , and 



Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively 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, excluding Alaska and Hawaii: (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. We also identify errors in the original building dataset. We evaluate precision and recall in the data for three large U.S. urban areas. Precision is high and comparable to results reported by Microsoft while recall is high for buildings with footprints larger than 200 m2 but lower for progressively smaller buildings.

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Publication type Article
Publication Subtype Journal Article
Title A rasterized building footprint dataset for the United States
Series title Scientific Data
DOI 10.1038/s41597-020-0542-3
Volume 7
Year Published 2020
Language English
Publisher Nature
Contributing office(s) Geosciences and Environmental Change Science Center
Description 207, 10 p.
Country United States
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