Can I read Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis on EtoBox?

Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis by He, Yutong; Wang, Dingjie; Lai, Nicholas; Zhang, William; Meng, Chenlin; Burke, Marshall; Lobell, David B.; Ermon, Stefano is a scholarly article available to read on EtoBox.

What is Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis about?

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.

Author

He, Yutong; Wang, Dingjie; Lai, Nicholas; Zhang, William; Meng, Chenlin; Burke, Marshall; Lobell, David B.; Ermon, Stefano

Published

2021

Language

EN