  Accurate local-level poverty measurement is an essential task for governments
and humanitarian organizations to track the progress towards improving
livelihoods and distribute scarce resources. Recent computer vision advances in
using satellite imagery to predict poverty have shown increasing accuracy, but
they do not generate features that are interpretable to policymakers,
inhibiting adoption by practitioners. Here we demonstrate an interpretable
computational framework to accurately predict poverty at a local level by
applying object detectors to high resolution (30cm) satellite images. Using the
weighted counts of objects as features, we achieve 0.539 Pearson's r^2 in
predicting village-level poverty in Uganda, a 31% improvement over existing
(and less interpretable) benchmarks. Feature importance and ablation analysis
reveal intuitive relationships between object counts and poverty predictions.
Our results suggest that interpretability does not have to come at the cost of
performance, at least in this important domain.
