Short Bio

Marc Rußwurm is Assistant Professor of Machine Learning and Remote Sensing at Wageningen University. His background is in Geodesy and Geoinformation, and he obtained a Ph.D. in Remote Sensing Technology at TU Munich. During his Ph.D., he could visit the European Space Agency and the University of Oxford as a participant in the Frontier Development Lab in 2018, the Obelix Laboratory in Vannes, and the Lobell Lab in Stanford. As a postdoctoral researcher, he joined the Environmental Computational Science and Earth Observation Laboratory at EPFL, Switzerland. His research interests are developing modern machine learning methods for real-world remote sensing problems, such as classifying vegetation from satellite time series and detecting marine debris in the oceans. He is interested in domain shifts and transfer learning problems naturally arising from geographic data.

News and Recent Publications

Selected Publications

A full list of publications available via google scholar

Teaching

I regularly teach in the following courses at Wageningen University.

  • Advanced Earth Observation (GRS-32306)
  • Machine Learning (FTE-35306)
  • Deep Learning (GRS-34806)

Academic CV

For a detailed list of talks, teaching activities, community engagement, and publications, please see my Academic CV

Apps and Links

Some projects, I work(ed) on.

SatCLIP - A pre-trained location encoder to express different geographic locations in vectors.
Siren(SH) LocationEncoders - A large comparative study of location encoders and the proposition of using Siren with Spherical Harmonic basis functions.
BreizhCrops - A benchmark dataset for crop type mapping.
Beat The MAML - An interactive interfact for few-shot land cover classification.
Marine Debris Explorer - An interactive Earthengine app to compare the performance of different marine debris detectors.
TSLearn - A machine learning library for time series. (minor contributions while working on ELECTS)