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	28 Feb 2023 12:41
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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