  Accurate and comprehensive measurements of a range of sustainable development
outcomes are fundamental inputs into both research and policy. We synthesize
the growing literature that uses satellite imagery to understand these
outcomes, with a focus on approaches that combine imagery with machine
learning. We quantify the paucity of ground data on key human-related outcomes
and the growing abundance and resolution (spatial, temporal, and spectral) of
satellite imagery. We then review recent machine learning approaches to
model-building in the context of scarce and noisy training data, highlighting
how this noise often leads to incorrect assessment of models' predictive
performance. We quantify recent model performance across multiple sustainable
development domains, discuss research and policy applications, explore
constraints to future progress, and highlight key research directions for the
field.
