Understanding economic development in rural Africa using satellite imagery, building footprints and deep models

Recent advancements in machine learning enable cost effective methods for understanding societal and economic activities in developing countries using publicly available satellite imagery. However, this progress remains stagnant in rural areas where the largest population under poverty line resides. In this work, we explore deep models' performance in rural areas in Africa and investigate methods that improve the performance. We argue that the geographic displacement noise present in ground surveys for anonymization purposes causes misalignments between input imagery and labels and therefore hampers accuracy, which exacerbates in rural areas. We then propose to incorporate building footprints data and a novel self-attention mechanism to provide more robust and accurate predictions of socioeconomic development. We test our framework against three socioeconomic measures in 21 African countries. Our best models outperform previous baselines in most of these tasks.