Marc Rußwurm

• Our METEOR paper, i.e., “Meta-learning to address diverse Earth observation problems across resolutions” was published at Nature Earth & Environment. We train a meta-learning model to adapt to few-shot remote sensing problems across different spectral, spatial resolutions.
• We investigate location encoding in “Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks”, which was selected as spotlight (top 5%!) at the International Conference on Learning Representations (ICLR)
• I am the main organizer of the second Machine Learning for Remote Sensing (ML4RS) workshop at ICLR 2024. See you in Vienna, May 11th!

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.

I am active in the IAPR Thematic Committee 7 Remote Sensing & Mapping and the ISPRS TC2 Working Group 5 (TCII-WG5)