Papers › In-domain representation learning for remote sensing
In-domain representation learning for remote sensing
Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | EuroSAT | ResNet50 | Accuracy (%) | 99.2 | #5 of 15 | Archive leaderboard | report |
| Image Classification | RESISC45 | ResNet50 | Top 1 Accuracy | 96.83 | #1 of 20 | Archive leaderboard | report |
| Image Classification | So2Sat LCZ42 | ResNet50 | Accuracy | 63.25 | #1 of 1 | Archive leaderboard | report |
| Multi-Label Image Classification | BigEarthNet | ResNet50 | mAP (macro) | 75.36 | #6 of 10 | Archive leaderboard | report |
| Scene Classification | UC Merced Land Use Dataset | ResNet50 | Accuracy (%) | 99.61 | #4 of 6 | Archive leaderboard | report |
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