Satellite images reveal enormous amounts of information about oncoming hurricanes, military troop movements and changes to the polar ice cap.

Thanks in part to the work of Stanford computer scientist Stefano Ermon, they can also help us understand and ultimately assist impoverished communities around the world. He recently embarked on a two-year study that builds on research in which his team created machine learning models to accurately infer poverty and wealth at the community level from satellite imagery. The model used things like nighttime light intensity, as well as features visible during the day, such as roads, tall buildings and even swimming pools, to accurately predict whether homes have access to electricity, piped water and sanitation. The program also predicts crop yields at harvest time and could help identify year-to-year changes that allow farmers to recognize and adapt to climate change.

Ermon’s approach begins by analyzing images of towns for which there is solid on-the-ground survey information. The program then teaches itself to find visual patterns, from color intensities to edges, that correlate with wealth or access to piped water. Over time, the program gets better at making predictions about social and economic conditions in areas that have no survey data.

It’s often difficult to explain why the system “sees” what it does, because it looks for patterns rather than specific features of the landscape. The program was never taught to recognize swimming pools, for example, but it nevertheless taught itself that swimming pools were tied to wealth and incorporated them into its model for mapping poverty.

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