{"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/lightweight-pre-trained-transformers-for","title":"Lightweight, Pre-trained Transformers for Remote Sensing Timeseries","arxiv_id":"2304.14065","date":"2023-04-27","proceeding":null,"authors":["Gabriel Tseng","Ruben Cartuyvels","Ivan Zvonkov","Mirali Purohit","David Rolnick","Hannah Kerner"],"abstract":"Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. 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