In-domain representation learning for remote sensing

Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standar...

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Bibliographic Details
Main Authors Neumann, Maxim, Pinto, Andre Susano, Zhai, Xiaohua, Houlsby, Neil
Format Journal Article
LanguageEnglish
Published 15.11.2019
Subjects
Computer Science - Computer Vision and Pattern Recognition
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Summary:Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote sensing representation learning. The established baselines achieve state-of-the-art performance on these datasets.
DOI:10.48550/arxiv.1911.06721