SpectralEarth-FM is a multisensor hierarchical transformer pretrained on a 40TB co-located HSI-MSI-SAR dataset using a JEPA-style objective and reports state-of-the-art results on hyperspectral and standard EO benchmarks.
Thor: A versatile foundation model for earth observation climate and society applications.arXiv preprint arXiv:2601.16011, 2026
2 Pith papers cite this work. Polarity classification is still indexing.
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Perspective paper calling for unified spatial representation learning that integrates raster imagery with vector semantics in geospatial foundation models.
citing papers explorer
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SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining
SpectralEarth-FM is a multisensor hierarchical transformer pretrained on a 40TB co-located HSI-MSI-SAR dataset using a JEPA-style objective and reports state-of-the-art results on hyperspectral and standard EO benchmarks.
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Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
Perspective paper calling for unified spatial representation learning that integrates raster imagery with vector semantics in geospatial foundation models.