Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
J., D UJARDIN , T., B OUNTOS , N
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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.
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Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Agentic AI faces structural challenges in remote sensing due to geospatial data properties and workflow constraints, requiring EO-native agents built around structured state, tool-aware reasoning, and validity-aware evaluation.
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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.