research papers from our team

monitoring maize yield variability over space and time

Monitoring Maize Yield Variability over Space and Time with Unsupervised Satellite Imagery Features

Cullen Molitor, Juliet Cohen, Grace Lewin, Steven Cognac, Protensia Hadunka, Jonathan Proctor, and Tamma Carleton (Remote Sensing, 2025)

Github repo

examining what is visible in satellite imagery

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 (NBER, 2025)

downscaling administrative data

Global high-resolution estimates of the United Nations Human Development Index using Satellite Imagery and Machine Learning

Luke Sherman, Jonathan Proctor, Hannah Druckenmiller, Heriberto Tapia & Solomon Hsiang (NBER, 2023)

data + code

investigating fairness and representation in satellite-based predictions

Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy

Emily Aiken, Esther Rolf & Joshua Blumenstock (IJCAI 2023)

using remotely sensed data in inference

Parameter Recovery Using Remotely Sensed Variables

Jonathan Proctor, Tamma Carleton, Sandy Sum (NBER, 2023)

introducing MOSAIKS

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 & Solomon Hsiang (Nature Communications, 2021)

sampling ground-truth

Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis

Ian Bolliger, Tamma Carleton, Solomon Hsiang, Jonathan Kadish, Jonathan Proctor, Benjamin Recht, Esther Rolf, Vaishaal Shankar (Bloomberg Data for Good Exchange Conference, 2017)