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Fernerkundung / Remote Sensing

  • C. Betancourt, T. Stomberg, S. Stadtler, R. Roscher, M. G. Schultz (2021)
    AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics
    Earth System Science Data Discussions
  • D. Tuia, R. Roscher, J. D. Wegner, N. Jacobs, X. Zhu, G. Camps-Valls (2021)
    Toward a Collective Agenda on AI for Earth Science Data Analysis
    IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 2, pp. 88-104.
  • Drees, L., Junker-Frohn, L. V., Kierdorf, J., Roscher, R. (2021)
    Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks
    Computers and electronics in agriculture 190, 106415.
  • I. Weber, J. Bongartz, R. Roscher (2021)
    Artificial and beneficial–Exploiting artificial images for aerial vehicle detection
    SPRS Journal of Photogrammetry and Remote Sensing, vol. 175, p. 158–170.
  • Mersch, B., Höllen, T., Zhao, K., Stachniss, C., Roscher, R. (2021)
    Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks
    2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4888-4895.
  • R. Roscher (2021)
    Deep Self-taught Learning in Remote Sensing
    Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 37-45.
  • Stomberg, T., Weber, I., Schmitt, M., Roscher, R. (2021)
    Ungle-Net: Using explainable machine learning to gain new insights into the appearance of wilderness in satellite imagery
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 317-324.
  • I. Weber, J. Bongartz, R. Roscher (2021)
    Artificial and beneficial–Exploiting artificial images for aerial vehicle detection
    ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, p. 158-170

 

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