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 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.
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EuroSAT
RESISC45
So2Sat LCZ42
Results from the Paper
Ranked #1 on
Multi-Label Image Classification
on BigEarthNet
(mAP (macro) metric)
