Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the targeting of humanitarian aid and the allocation of government resources. These maps are typically constructed by training machine learning algorithms with country- or continent-scale data, but many real-world applications are focused on specific urban or rural areas. This paper shows that satellite-based poverty predictions are less accurate at distinguishing levels of wealth within urban and rural areas than they are at distinguishing wealth differences between urban and rural areas, investigates why this may be the case, and documents the implications of these disparities for downstream policy decisions.