for extracting socioeconomic indicators from raw satelliteimagery. In the past ﬁve years, deep learning approachesapplied to large-scale datasets such as ImageNet have rev-olutionized the ﬁeld of computer vision, leading to dramaticimprovements in fundamental tasks such as object recogni-tion (Russakovsky et al. 2014). However, the use of con-temporary techniques for the analysis of remote sensing im-agery is still largely unexplored. Modern approaches suchas Convolutional Neural Networks (CNN) can, in principle,be directly applied to extract socioeconomic factors, but theprimary challenge is a lack of training data. While such datais readily available in the United States and other developednations, it is extremely scarce in Africa where these tech-niques would be most useful.