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Physically Typed and Geometry-Aware Representations for Earth Foundation Models

Ranjan, Rajiv
arXiv (OAI Expanded) · Papers
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computer-vision-and-pattern-recognitionmachine-learning
Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while modern weather and climate models have demonstrated that Earth-specific geometry, spherical operators, meshes, and hybrid physical solvers can materially
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