  High-resolution satellite imagery has proven useful for a broad range of
tasks, including measurement of global human population, local economic
livelihoods, and biodiversity, among many others. Unfortunately,
high-resolution imagery is both infrequently collected and expensive to
purchase, making it hard to efficiently and effectively scale these downstream
tasks over both time and space. We propose a new conditional pixel synthesis
model that uses abundant, low-cost, low-resolution imagery to generate accurate
high-resolution imagery at locations and times in which it is unavailable. We
show that our model attains photo-realistic sample quality and outperforms
competing baselines on a key downstream task -- object counting -- particularly
in geographic locations where conditions on the ground are changing rapidly.
