Using Deep Learning to Track Poverty with Satellites

Researchers at Stanford University are utilizing artificial intelligence (AI) to identify areas of poverty in hard-to-reach places. Publishing their research in Science Magazine, the team consisting of Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, and Stefano Ermon used deep learning algorithms to sort through millions of satellite images to identify economic conditions in five African countries. This research supports a forecast Tractica made over a year ago in our Artificial Intelligence for Enterprise Applications report, that spending on AI software by philanthropy organizations will grow dramatically over the next 10 years. Traditionally, philanthropy organizations have conducted door-to-door surveys to identify people living in poverty, but these surveys are imprecise, time-consuming, and expensive. Many international aid organizations including the World Bank have been trying to use satellite surveys to gather data remotely on developing countries, but the expense of gathering and analyzing this data using conventional methods has proven prohibitive.