Corpus ID: 208076837

In-domain representation learning for remote sensing

@article{Neumann2019IndomainRL,
  title={In-domain representation learning for remote sensing},
  author={M. Neumann and Andr{\'e} Susano Pinto and Xiaohua Zhai and N. Houlsby},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.06721}
}
  • M. Neumann, André Susano Pinto, +1 author N. Houlsby
  • Published 2019
  • Computer Science
  • ArXiv
  • 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… CONTINUE READING
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