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Knowledge of building density is vital for policy-making, community planning, understanding environmental impacts, delivering services and responding to emergencies, but current information is often unavailable in countries that are low on resources.
Building footprints are a key ingredient for estimating population density, so Google teams trained a deep learning model to determine the footprints of buildings from high resolution satellite imagery. The AI model detects buildings in dense areas and the most rural areas, which can be challenging due to unique shapes and materials. Each building detected also includes a Plus Code that acts as an address, provides location information, and estimates the size of the building.
The data set contains more than 500 million building detections across 64% of the African continent, allowing for better community planning and emergency response. Open Buildings supports governments, nonprofits, researchers and the public with applications such as disaster response, household survey planning, electricity provision and urban planning.
“When I lived in Rwanda for two years, there was unprecedented rainfall that destroyed fields and homes. I imagine this data set could help governments and orgs better identify high-risk areas and offer potentially life-saving assistance.”