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
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. ISPRS Journal of Photogrammetry and Remote Sensing, 169:421 – 435. 2020
In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 788–796. EarthVision 2020 Best Paper Award.
Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019. 2019..
ISPRS International Journal of Geo-Information, 2018.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2017. (best paper award)