Google Open Buildings addresses one of the most fundamental gaps in global geospatial data: the absence of building footprints across much of Africa, South Asia, and Southeast Asia. In many of these regions, fewer than 20% of structures appear in OpenStreetMap, and official cadastral mapping either doesn't exist or isn't publicly available.
By applying deep learning to high-resolution satellite imagery, Google produced footprints for over 1.8 billion buildings — creating, for many rural communities, the first building-level map that has ever existed. This kind of baseline data is prerequisite for work that the rest of the GIS world takes for granted: population estimation, electrification planning, disaster damage assessment, and infrastructure gap analysis.
The dataset complements rather than replaces OpenStreetMap. OSM carries richer attributes — building type, height, use — but has uneven coverage that depends on volunteer mapping effort. Google Open Buildings provides far more complete spatial coverage but only includes footprint geometry, area, and a confidence score.
For GIS analysts working in covered regions, the practical approach is to use both: Open Buildings as the comprehensive spatial layer and OSM for the subset of structures where attribute detail has been contributed. Building footprints also serve as a powerful proxy layer — aggregate them by grid cell or administrative unit and you get settlement density maps that pair with population grids, health facility locations, and infrastructure networks for planning and humanitarian response.
