arXiv Computer Vision
Oct 21

ICIBS: Increasing the Context for Ice Boundary Segmentation

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
arXiv Machine Learning
Aug 6

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series

arXiv:2608. 04706v1 Announce Type: new Abstract: Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to make informed decisions in a phase of increasing deployment activities.

By Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer