Researchers
Foundation models for Earth observation
Earth-observation data spans many sensors, spatial resolutions, spectral ranges, and acquisition conditions. Rather than expecting a single model to cover this diversity, we investigate how specialized foundation models can share knowledge and provide transferable representations for a range of downstream tasks.
Our work focuses on self-supervised and contrastive representation learning for optical and multispectral imagery. We explore cross-modal distillation from optical vision foundation models, the combination of complementary teachers, and efficient adaptation to new sensors without discarding useful pretrained knowledge. The goal is to build representations that remain effective across tasks such as semantic segmentation, classification, and change detection while reducing the need for large annotated datasets.