THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications

• URL: http://arxiv.org/abs/2601.16011v1
• Date: Thu, 22 Jan 2026 14:38:00 GMT
• Title: THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications
• Abstract summary: THOR is a "computeadaptive" foundation model that solves both input heterogeneity and deployment rigidity.<n>We pre-train THOR with a novel randomized patch and input image size strategy.<n>This allows a single set of pre-trained weights to be deployed at inference with any patch size, enabling a dynamic trade-off between computational cost and feature resolution without retraining.
• Score: 9.852915112122567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Earth observation foundation models are architecturally rigid, struggle with heterogeneous sensors and are constrained to fixed patch sizes. This limits their deployment in real-world scenarios requiring flexible computeaccuracy trade-offs. We propose THOR, a "computeadaptive" foundation model that solves both input heterogeneity and deployment rigidity. THOR is the first architecture to unify data from Copernicus Sentinel-1, -2, and -3 (OLCI & SLSTR) satellites, processing their native 10 m to 1000 m resolutions in a single model. We pre-train THOR with a novel randomized patch and input image size strategy. This allows a single set of pre-trained weights to be deployed at inference with any patch size, enabling a dynamic trade-off between computational cost and feature resolution without retraining. We pre-train THOR on THOR Pretrain, a new, large-scale multi-sensor dataset and demonstrate state-of-the-art performance on downstream benchmarks, particularly in data-limited regimes like the PANGAEA 10% split, validating that THOR's flexible feature generation excels for diverse climate and society applications.

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