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.
@article{arxiv.1911.06721,
title = {In-domain representation learning for remote sensing},
author = {Maxim Neumann and Andre Susano Pinto and Xiaohua Zhai and Neil Houlsby},
journal= {arXiv preprint arXiv:1911.06721},
year = {2019}
}