Empirical study finds synthetic-to-real domain gap sharply degrades diffusion SR models on real cross-sensor satellite pairs while real-data training faces optimization and adaptation problems.
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GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.
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FireDataForge automates retrieval and harmonization of 11 multi-source wildfire geospatial datasets into common-grid NumPy arrays for a given MTBS Event ID.
VibrantForests produces coherent 10m wall-to-wall estimates of multiple forest structure attributes across the US by applying satellite models trained on lidar samples.
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
Earth embeddings from satellite images predict neighborhood-level urban indicators with higher accuracy for built-environment outcomes than for behavior-driven ones, showing city-specific variation but year-to-year stability.
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