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.
By Marcel Dreier, Dakota Pyles, Nora Gourmelon, Moritz Koch, Fei Wu, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein
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...
arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.
By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2604. 09922v2 Announce Type: replace Abstract: Subsurface stratigraphy contains important spatio-temporal information about accumulation, deformation, and layer formation in polar ice sheets.
By Zesheng Liu, Maryam Rahnemoonfar
arXiv:2607. 04117v1 Announce Type: new Abstract: ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones.
By Arunkumar Ramachandran
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
By Pedro Sousa (Department of Computer Science, University of Cambridge), Will Tebbutt (Department of Engineering, University of Cambridge), Sadiq Jaffer (Department of Computer Science, University of Cambridge), Robin Young (Department of Computer Science, University of Cambridge), Anil Madhavapeddy (Department of Computer Science, University of Cambridge), Richard E. Turner (Department of Engineering, University of Cambridge)
Cryo-Bench is a new benchmark that evaluates foundation models for cryosphere mapping, comprising six semantic‑segmentation datasets across five cryospheric components (supraglacial debris, glacial lakes, sea ice, calving fronts, and Antarctic ice‑shelf extent). The benchmark includes multispectral, RGB, and SAR observations from under‑represented regions and tests thirteen geo‑foundation models alongside U‑Net and Vision Transformer baselines. Results show that with frozen encoders U‑Net slightly outperforms TerraMind, but the difference is not statistically significant; fine‑tuning with learning‑rate optimization can dramatically improve performance for some models, while in few‑shot scenarios several foundation models retain over 90 % of their full‑label accuracy.
By Saurabh Kaushik, Lalit Maurya, Beth Tellman, Swalpa Kumar Roy, Valerio Marsocci, Gustau Camps-Valls, Jocelyn Chanussot
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
By Wiktor Kamzela, Jakub Kubiak, Adam Dobosz, J\k{e}drzej Miczke, Anatol Kaczmarek, Piotr Wyrwi\'nski, Wojciech Stefaniak, Wojciech Kot{\l}owski
arXiv:2607. 04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularly in mountainous regions where temperature can vary by several degrees over short distances.
By Francisco Passos
arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.
By Shuang Liu, Fiona Johnson, Rohitash Chandra
arXiv:2609.16347v1 Announce Type: new
Abstract: Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data f...
By Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett, Farnoush Banaei-Kashani