Corpus ID: 208076837

In-domain representation learning for remote sensing

  title={In-domain representation learning for remote sensing},
  author={M. Neumann and Andr{\'e} Susano Pinto and Xiaohua Zhai and N. Houlsby},
  • 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
    4 Citations

    Figures, Tables, and Topics from this paper.

    The color out of space: learning self-supervised representations for Earth Observation imagery
    Deep Learning Meets SAR
    • 1
    • PDF
    TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
    • 1
    • Highly Influenced
    • PDF


    Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
    • 392
    • PDF
    Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
    • 580
    • PDF
    Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
    • 132
    • PDF
    Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
    • 189
    • PDF
    Towards better exploiting convolutional neural networks for remote sensing scene classification
    • 464
    • PDF
    Remote Sensing Image Scene Classification: Benchmark and State of the Art
    • 539
    • PDF
    Multiview Deep Learning for Land-Use Classification
    • 167
    • PDF
    Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
    • 37
    • PDF
    Domain Adaptive Transfer Learning with Specialist Models
    • 39
    • PDF
    Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
    • 290
    • PDF