TESSERA learns robust label-efficient embeddings from irregular multi-modal EO time series via Barlow Twins plus global shuffling and mix-based regularizers, delivering SOTA accuracy on classification, segmentation and regression tasks while releasing planetary-scale embeddings and code.
J., D UJARDIN , T., B OUNTOS , N
2 Pith papers cite this work. Polarity classification is still indexing.
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