Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to speed up innovation in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. We specifically explore in-domain representation learning and address the question of "what characteristics should a dataset have to be a good source for remote sensing representation learning". The established baselines achieve state-of-the-art performance on these datasets. 

Exploration of in-domain representation learning for remote sensing datasets. This paper provided several standardized remote sensing data sets and showed that in-domain representation could produce better baseline results for remote sensing compared to fine-tuning on ImageNet or learning from scratch.