A Global Census of Solar Facilities Using Deep Learning and Remote Sensing (Papers Track) Honorable Mention
Lucas Kruitwagen (University of Oxford); Kyle Story (Descartes Labs); Johannes Friedrich (World Resource Institute); Sam Skillman (Descartes Labs); Cameron Hepburn (University of Oxford)
Abstract
We present a comprehensive global census of solar power facilities using deep learning and remote sensing. We search imagery from the Airbus SPOT 6/7 and European Space Agency Sentinel-2 satellites covering more than 48% of earth’s land-surface using a combination of deep-learning models, image processing, and hand-verification. We locate solar facilities and measure their footprints and installation dates. The resulting dataset of 68,797 facilities has an estimated generating capacity of 209 GW; 78% of this capacity was not previously reported in public databases. These asset-level data are critical for understanding energy infrastructure, evaluate climate risk, and efficiently use intermittent solar energy - ultimately enabling the transition to a predominantly renewable energy system.