Research Interests

I aim to develop state-of-the-art machine learning methods for meaningful and important applications, such the classification of vegetation from satellite time series or the detection of marine debris on the oceans. My Ph.D. research focused on deep learning models for satellite time series classification and crop type mapping. Today, I deploy general deep learning models on a global scale which requires tackling distribution shift with transfer learning on a variety of different applications. In particular, I train deep learning models with the model-agnostic meta-learning algorithm (MAML) to tackle a variety of different problems with few labelled samples.

Selected Publications

A full list of publications available via google scholar

  • Towards detecting floating objects on a global scale with learned spatial features using Sentinel 2
  • Mifdal, J., Carmo R., Rußwurm M.
    ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 285–293, 2021, 169:421 – 435.
  • Self-attention for raw optical satellite time series classification
  • Rußwurm, M. and Körner, M.
    Self-attention for raw optical satellite time series classification. ISPRS Journal of Photogrammetry and Remote Sensing, 169:421 – 435. 2020
  • Meta-learning for few-shot land cover classification
  • Rußwurm, M., Wang, S., Korner, M., and Lobell, D.
    In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 788–796. EarthVision 2020 Best Paper Award.
  • Segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery
  • Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopačková, Piotr Bilinski
    Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019. 2019..
  • Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
  • Marc Rußwurm and Marco Körner
    ISPRS International Journal of Geo-Information, 2018.
  • Temporal Vegetation Modelling using Long Short-Term Memory Networks for Crop Identification from Medium-Resolution Multi-Spectral Satellite Images
  • Marc Rußwurm and Marco Körner
    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017. (best paper award)