A generalizable and accessible approach to machine learning with global satellite imagery

Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht and Solomon Hsiang ()
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Esther Rolf: Electrical Engineering & Computer Science Department
Jonathan Proctor: Harvard University
Tamma Carleton: UC Santa Barbara
Ian Bolliger: Goldman School of Public Policy
Vaishaal Shankar: Electrical Engineering & Computer Science Department
Miyabi Ishihara: Goldman School of Public Policy
Benjamin Recht: Electrical Engineering & Computer Science Department
Solomon Hsiang: Goldman School of Public Policy

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.

Date: 2021
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DOI: 10.1038/s41467-021-24638-z

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