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arXiv Computer Vision By Marcel Dreier, Dakota Pyles, Nora Gourmelon, Moritz Koch, Fei Wu, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein

ICIBS: Increasing the Context for Ice Boundary Segmentation

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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