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Poster
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Yutong He · Dingjie Wang · Nicholas Lai · William Zhang · Chenlin Meng · Marshall Burke · David Lobell · Stefano Ermon

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @
in Poster Session 4 »

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.

Keywords: Generative Model · Graph Learning

Author Information

Yutong He (Computer Science Department, Stanford University)
Dingjie Wang (Stanford University)
Nicholas Lai (Stanford University)
William Zhang (Stanford University)
Chenlin Meng (Stanford University)
Marshall Burke (Stanford University)
David Lobell (Stanford University)
Stefano Ermon (Stanford)

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