research papers from our team

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)

• Github repo

• Code Ocean implementation

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)

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

using remotely sensed data in inference

Parameter Recovery Using Remotely Sensed Variables

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

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)