Stefano Ermon: Measuring Economic Development from Space with Machine Learning

Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to climate adaptation strategies. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. A key challenge, however, is the lack of large quantities of labeled data that often characterize successful machine learning applications. In this talk, I will present new approaches for learning useful spatio-temporal models in contexts where labeled training data is scarce or not available at all. I will show applications to predict and map poverty in developing countries, monitor agricultural productivity and food security outcomes, and map infrastructure access in Africa. Our methods can reliably predict economic well-being using only high-resolution satellite imagery. Because images are passively collected in every corner of the world, our methods can provide timely and accurate measurements in a very scalable end eco