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Presenter: Arnt-Børre Salberg, Chief Research Scientist, Dept. for Image Analysis, Machine Learning and Earth Observation, Norwegian Computing Center
As deep learning transforms earth observation (EO) analysis, foundation models offer a promising alternative to traditional supervised learning by addressing data labeling challenges through large-scale, self-supervised learning.
The FM4CS model, developed for the European Space Agency, is a versatile multimodal foundation model tailored for climate and society EO applications. It supports four different Sentinel sensors: Sentinel-1 SAR, Sentinel-2 MSI, Sentinel-3 OLCI, and Sentinel-3 SLSTR, with resolutions ranging from 10 m to 1000 m. Evaluations across various benchmark EO tasks demonstrate FM4CS's robustness and adaptability, establishing it as a strong foundation for diverse EO applications.
This seminar is open for members of the consortium. If you want to participate as a guest please sign up.
Arnt-Børre Salberg, Chief Research Scientist, Dept. for Image Analysis, Machine Learning and Earth Observation, Norwegian Computing Center
This seminar is open for members of the consortium. If you want to participate as a guest please sign up.
Arnt-Børre Salberg, Chief Research Scientist, Dept. for Image Analysis, Machine Learning and Earth Observation, Norwegian Computing Center
This seminar is open for members of the consortium. If you want to participate as a guest please sign up.