What Can Satellite Imagery and Machine Learning Measure? / Jonathan Proctor, Tamma Carleton, Trinetta Chong, Taryn Fransen, Simon Greenhill, Jessica Katz, Hikari Murayama, Luke Sherman, Jeanette Tseng, Hannah Druckenmiller, Solomon Hsiang.

Format:

Book

Author/Creator:

Proctor, Jonathan.

Contributor:

Carleton, Tamma.

Chong, Trinetta.

Fransen, Taryn.

Greenhill, Simon.

Katz, Jessica.

Murayama, Hikari.

Sherman, Luke.

Tseng, Jeanette.

Druckenmiller, Hannah.

Hsiang, Solomon.

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w34315.

NBER working paper series no. w34315

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2025.

Summary:

Satellite imagery and machine learning (SIML) are increasingly being combined to remotely measure social and environmental outcomes, yet use of this technology has been limited by insufficient understanding of its strengths and weaknesses. Here, we undertake the most extensive effort yet to characterize the potential and limits of using a SIML technology to measure ground conditions. We conduct 115 standardized large-scale experiments using a composite high-resolution optical image of Earth and a generalizable SIML technology to evaluate what can be accurately measured and where this technology struggles. We find that SIML alone predicts roughly half the variation in ground measurements on average, and that variables describing human society (e.g. female literacy, R²=0.55) are generally as easily measured as natural variables (e.g. bird diversity, R²=0.55). Patterns of performance across measured variable type, space, income and population density indicate that SIML can likely support many new applications and decision-making use cases, although within quantifiable limits.

Notes:

October 2025.

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