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.
Ringmo-lite: A remote sensing multi-task lightweight network with cnn-transformer hybrid framework, 2023, 2309.09003
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
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.