High-resolution Global Building Emissions Estimation using Satellite Imagery (Proposals Track)

Paul J Markakis (Duke University); Jordan Malof (University of Montana); Trey Gowdy (Duke University); Leslie Collins (Duke University); Aaron Davitt (WattTime); Gabriela Volpato (WattTime); Kyle Bradbury (Duke University)

Paper PDF Poster File NeurIPS 2023 Poster Cite
Buildings Computer Vision & Remote Sensing


Globally, buildings account for 30% of end-use energy consumption and 27% of energy sector emissions, and yet the building sector is lacking in low-temporal-latency, high-spatial-resolution data on energy consumption and resulting emissions. Existing methods tend to either have low resolution, high latency (often a year or more), or rely on data typically unavailable at scale (such as self-reported energy consumption). We propose a machine learning based bottom-up model that combines satellite-imagery-derived features to compute Scope 1 global emissions estimates both for residential and commercial buildings at a 1 square km resolution with monthly global updates.