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.
@article{arxiv.2106.11485,
title = {Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis},
author = {Yutong He and Dingjie Wang and Nicholas Lai and William Zhang and Chenlin Meng and Marshall Burke and David B. Lobell and Stefano Ermon},
journal= {arXiv preprint arXiv:2106.11485},
year = {2022}
}