  Poverty maps derived from satellite imagery are increasingly used to inform
high-stakes policy decisions, such as the allocation of humanitarian aid and
the distribution of government resources. Such poverty maps are typically
constructed by training machine learning algorithms on a relatively modest
amount of ``ground truth" data from surveys, and then predicting poverty levels
in areas where imagery exists but surveys do not. Using survey and satellite
data from ten countries, this paper investigates disparities in representation,
systematic biases in prediction errors, and fairness concerns in
satellite-based poverty mapping across urban and rural lines, and shows how
these phenomena affect the validity of policies based on predicted maps. Our
findings highlight the importance of careful error and bias analysis before
using satellite-based poverty maps in real-world policy decisions.
