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.
@inproceedings{he2021spatial,
title={Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis},
author={He, Yutong and Wang, Dingjie and Lai, Nicholas and Zhang, William and Meng, Chenlin and Burke, Marshall and Lobell, David B. and Ermon, Stefano},
year={2021},
month={December},
abbr={NeurIPS 2021},
booktitle={Neural Information Processing Systems},
}