Linus Scheibenreif
Hi I'm Linus. I'm currently a Ph.D. Candidate in Computer Science with the AI:ML group at the University of St. Gallen (HSG), Switzerland.
My work combines Machine Learning and Computer Vision methods with Geospatial Earth Observation. For my Ph.D., I focus on Self-supervised Deep Learning for Earth Observation data.
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News
- I will be an AI Resident with Google X in Mountain View working on geospatial machine learning software from Jan.-May 2024.
- New paper on Parameter Efficient Self-supervised Domain Adaptation for Geospatial Foundation Models accepted at CVPR 2024 (paper, code)!
- We will hold the second iteration of our tutorial on Data-efficient Deep Learning for Earth Observation at IGARSS 2024 in Athens.
- Three papers accepted at IGARSS 2024. Congratulations to our students!
- New paper on Masked Vision Transformers for Hyperspectral data accepted at the CVPR 2023 EarthVision Workshop!
- Tutorial on Data-efficient Deep Learning for Earth Observation accepted at IGARSS 2023.
- Three papers, including the BEN-GE dataset, accepted at IGARSS 2023.
- We hosted the Swiss Remote Sensing Days 2023 in St.Gallen!
- Finished the course phase of my Ph.D. Big thanks to my committee, Profs. Damian Borth, Michael Mommert, and Konrad Schindler!
- Our EarthVision 2022 paper won Best Student Paper Award (link)!
- New paper on Self-supervised Vision Transformers accepted at the CVPR 2022 EarthVision Workshop!
- New paper on Contrastive Self-supervised Data Fusion accepted to the ISPRS Annals!
- Our paper on Estimation of Ambient NO2 Concentrations was published in the IEEE Transactions on Geoscience and Remote Sensing!
- New paper on Invariant Risk Minimization for Cross-organism Inference accepted at NeurIPS 2021 Workshop Machine Learning for Health. This is an outcome of FDL together with researchers from NASA and Intel.
- I will be a Researcher with the Frontier Development Lab in the summer of 2021!
- New paper on Air Pollution Estimation from Space accepted at the ICML 2021 Workshop on Tackling Climate Change with Machine Learning.
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