SOURCE-LINKED INTELLIGENCE
Physically Typed and Geometry-Aware Representations for Earth Foundation Models
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 improve prediction. Yet these two advances are not equivalent. A conventional latent embedding has no inherent physical transformation law, whereas scalar fields, tangent polar-vector fields, axial/pseudovector quantities, covectors, and higher-order tensors transform differently under rotations, re
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T10:42:59.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.