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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

High-resolution Global Building Emissions Estimation using Satellite Imagery

Paul Markakis · Jordan Malof · Trey Gowdy · Leslie Collins · Dr. Aaron Davitt · Gabriela Volpato · Kyle Bradbury


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.

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