MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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The paper introduces ICIBS, a deep‑learning approach that enhances ice‑boundary segmentation by incorporating additional spatial and geo‑spatial context into the model. It reports state‑of‑the‑art performance on the IceAnatomy benchmark, achieving mean errors of 10.9 m (FAU subset) and 43.4 m (CReSIS subset), and demonstrates strong generalisation to new Patagonia data, outperforming baselines by 49.6 %. The authors provide the model code and dataset publicly on GitHub and Zenodo.
Mountain glaciers and ice sheets are undergoing rapid loss of ice mass. To assess their current volume and ice-thickness distribution, and to accurately forecast their future, ground-penetrating radar...
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