Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
Abstract
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.
BibTeX
@inproceedings{
he2021spatialtemporal,
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},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=IKz9uYkf3vZ}
}