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The paper presents a principled construction to convert CNNs and transformers into resolution-agnostic neural operators, enabling models trained at one grid resolution to generalize to others, crucial for scientific ML applications like PDE solving.
UniverSat introduces a Universal Patch Encoder for Vision Transformers that enables robust, sensor-agnostic spatial feature extraction across diverse Earth Observation data types, achieving strong results on classification and segmentation benchmarks.