Human Geography Commons^™

Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz

Economics Faculty Articles and Research

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These “poverty maps” are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 m2 and 50 cm2 respectively, covering all 2 million km2 of Mexico. Benchmark poverty estimates come from …