Papers › Self-supervised Learning in Remote Sensing: A Review
Self-supervised Learning in Remote Sensing: A Review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.
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Code
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Code Syntology ran Syntology
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55120f2026b56aa2 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | EuroSAT | MoCo-v2 (ResNet18, fine tune) | Accuracy (%) | 98.9 | #7 of 15 | Archive leaderboard | report |
| Image Classification | EuroSAT | MoCo-v2 (ResNet18, linear eval) | Accuracy (%) | 94.4 | #15 of 15 | Archive leaderboard | report |
| Multi-Label Image Classification | BigEarthNet | MoCo-v2 (ResNet18, fine tune) | mAP (micro) | 89.3 | #3 of 10 | Archive leaderboard | report |
| Multi-Label Image Classification | BigEarthNet | MoCo-v2 (ResNet18, fine tune) | official split | No | #3 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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