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

No 28045, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C02 C8 O13 O18 Q5 R1 (search for similar items in EconPapers)
Date: 2020-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-geo
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Published as Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2021. "A generalizable and accessible approach to machine learning with global satellite imagery," Nature Communications, vol 12(1).

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