Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis

• URL: http://arxiv.org/abs/2106.11485v1
• Date: Tue, 22 Jun 2021 02:16:24 GMT
• Title: Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
• Authors: Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
• Abstract summary: We propose a new conditional pixel synthesis model that uses abundant, low-cost, low-resolution imagery to generate accurate high-resolution imagery. We show that our model attains photo-realistic sample quality and outperforms competing baselines on a key downstream task -- object counting.
• Score: 66.50914391487747
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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