  The combination of high-resolution satellite imagery and machine learning
have proven useful in many sustainability-related tasks, including poverty
prediction, infrastructure measurement, and forest monitoring. However, the
accuracy afforded by high-resolution imagery comes at a cost, as such imagery
is extremely expensive to purchase at scale. This creates a substantial hurdle
to the efficient scaling and widespread adoption of high-resolution-based
approaches. To reduce acquisition costs while maintaining accuracy, we propose
a reinforcement learning approach in which free low-resolution imagery is used
to dynamically identify where to acquire costly high-resolution images, prior
to performing a deep learning task on the high-resolution images. We apply this
approach to the task of poverty prediction in Uganda, building on an earlier
approach that used object detection to count objects and use these counts to
predict poverty. Our approach exceeds previous performance benchmarks on this
task while using 80% fewer high-resolution images. Our approach could have
application in many sustainability domains that require high-resolution
imagery.
