{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatial-temporal-super-resolution-of","title":"Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis","arxiv_id":"2106.11485","date":"2021-06-22","proceeding":"NeurIPS 2021 12","authors":["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. 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