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cs.CV2025★ 1 cited
MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
Antoine Labatie, Michael Vaccaro, Nina Lardiere +2
Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We ta…
cs.CV2025
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping
Anatol Garioud, Sébastien Giordano, Nicolas David +1
The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these dat…