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Tuesday May 23, 2017


Can Satellites Learn to 'See' Poverty?

For the last few decades, and almost since astronauts first captured images of the nocturnal Earth, researchers have recognized that “night lights” data indirectly indexes the wealth of people producing the light. This econometric power seems to work across the planet: Not only do cities glow brighter than farmland, but American cities outshine Indian cities; and as a country’s GDP increases, so does its nighttime luminosity. Two years ago, a Stanford professor even used night lights data to show that North Korean leaderswere passing the costs of international economic sanctionsdown to farmers and villagers. As foreign governments imposed sanctions, Pyongyang became brighter and light from the hinterlands waned.

Night lights, therefore, appear to be an incredible resource. So much so that in countries with poor economic statistics, they can serve as aproxy for a regional wealth survey—except no one has to go house to house, running through a questionnaire. Yet research has also shown this not-a-survey will remain inexact: To a satellite at night, a few well-lit mansions and a dense but poorly lit shantytown can look nearly the same.

A new paper from a team at Stanford, published last week inScience,applies a trendy technique to this tricky problem. In order to make night lights more discerning, engineers and computer scientists fed a convolutional neural net—a standard type of artificial intelligence program—a series of data sets. They wanted to give it the insight of the night-light data while freeing it of its pitfalls.

First, they taught the neural net a generic image-recognition program that let it distinguish edges, corners, and more than 1,000 common objects. Second, they asked it to correlate a set of night lights data for a country with a daytime map of the same country, essentially teaching it what kind of features on the ground are more likely to make the surface brighter at night. Finally, they fed it the highest-resolution household-wealth data that exists for that country, theWorld Bank’s Living Standards Measurement Study, indexed to latitude and longitude.

“Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans – things like roads, urban areas and farmland,” said Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

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