  Object detection in high-resolution satellite imagery is emerging as a
scalable alternative to on-the-ground survey data collection in many
environmental and socioeconomic monitoring applications. However, performing
object detection over large geographies can still be prohibitively expensive
due to the high cost of purchasing imagery and compute. Inspired by traditional
survey data collection strategies, we propose an approach to estimate object
count statistics over large geographies through sampling. Given a cost budget,
our method selects a small number of representative areas by sampling from a
learnable proposal distribution. Using importance sampling, we are able to
accurately estimate object counts after processing only a small fraction of the
images compared to an exhaustive approach. We show empirically that the
proposed framework achieves strong performance on estimating the number of
buildings in the United States and Africa, cars in Kenya, brick kilns in
Bangladesh, and swimming pools in the U.S., while requiring as few as 0.01% of
satellite images compared to an exhaustive approach.
