UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation
Vision Transformers (ViT) dominate computer vision. However, their reliance on rigid patch projectors hinders transfer to Earth Observation (EO), where input modalities, scales, and resolutions vary widely. We introduce UniverSat, a ViT-style backbone built around a Universal Patch Encoder that maps patches from arbitrary spatial, spectral, and temporal resolutions, and from both optical and non-optical sensors, into a shared embedding space with a shared set of weights. This enables training a single model on heterogeneous multimodal corpora via self-supervision, yielding robust, sensor-agnostic spatial features. We validate this approach with strong results across classification and seg...
| Authors | Yohann Perron, Guillaume Astruc, Nicolas Gonthier et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
Connected in the knowledge graph
- g-astruc/UniverSatModel · cited by
- Major-TOM/Core-S2L2A-UniverSatDataset · cited by
- Major-TOM/Core-S1RTC-UniverSatDataset · cited by
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