SOTAVerified

In-domain representation learning for remote sensing

2019-11-15Code Available0· sign in to hype

Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby

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Abstract

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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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
EuroSATResNet50Accuracy (%)99.2Unverified
RESISC45ResNet50Top 1 Accuracy96.83Unverified
So2Sat LCZ42ResNet50Accuracy63.25Unverified

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