Papers › In-domain representation learning for remote sensing

In-domain representation learning for remote sensing

15 Nov 2019arXiv:1911.06721archive 2025-07-28

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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Image ClassificationMulti-Label Image ClassificationRepresentation LearningScene Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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