Calving front position variation of marine-terminating glaciers is an indicator of ice mass loss and a crucial parameter in numerical glacier models. Deep Learning (DL) systems can automatically extract this position from Synthetic Aperture Radar (SAR) imagery, enabling continuous, weather- and illumination-independent, large-scale monitoring. This study presents the first comparison of DL systems on a common calving front benchmark dataset. A multi-annotator study with ten annotators is performed to contrast the best-performing DL system against human performance. The best DL model's outputs deviate 221 m on average, while the average deviation of the human annotators is 38 m. This significant difference shows that current DL systems do not yet match human performance and that further research is needed to enable fully automated monitoring of glacier calving fronts. The study of Vision Transformers, foundation models, and the inclusion and processing strategy of more information are identified as avenues for future research.

本研究针对海洋终止冰川的崩裂前沿位置变化这一问题，提出使用深度学习系统从合成孔径雷达图像中自动提取该位置。这项研究的主要发现是，当前深度学习系统的表现与人类观察者存在显著差距，这表明需要进一步研究以实现冰川崩裂前沿的完全自动化监测。

比较研究：利用深度学习在合成孔径雷达图像中 delineation 冰川崩裂前沿