Multi-modal Self-supervised Learning for Earth Observation

Item Type Conference or Workshop Item (Other)
Abstract An ever-increasing number of Earth Observation satellites continually captures massive amounts of remote sensing data. This wealth of data makes manual analysis of all images by human experts impossible. At the same time, the data lacks readily available labels which are necessary for training supervised machine learning models, including state-of-the art deep learning approaches. Techniques from self-supervised machine learning make it possible to leverage unlabeled data for the training of deep neural network models, and thus improve our Earth Observation capabilities across different tasks.
Authors Scheibenreif, Linus Mathias; Hanna, Joëlle; Mommert, Michael & Borth, Damian
Research Team AIML Lab
Language English
Subjects computer science
HSG Profile Area None
Date 13 October 2022
Event Title AI4EO Symposium
Event Location Munich
Event Dates 13-14 Oct. 2022
Depositing User Linus Mathias Scheibenreif
Date Deposited 06 Dec 2022 08:55
Last Modified 06 Dec 2022 08:55
URI: https://www.alexandria.unisg.ch/publications/268262

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Scheibenreif, Linus Mathias; Hanna, Joëlle; Mommert, Michael & Borth, Damian: Multi-modal Self-supervised Learning for Earth Observation. [Conference or Workshop Item]

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https://www.alexandria.unisg.ch/id/eprint/268262
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