During benchmarking, the state-of-the-art model for glacier calving front delineation achieves near-human performance. However, when applied in a real-world setting at a novel study site, its delineation accuracy is insufficient for calving front products intended for further scientific analyses. This site represents an out-of-distribution domain for a model trained solely on the benchmark dataset. By employing a few-shot domain adaptation strategy, incorporating spatial static prior knowledge, and including summer reference images in the input time series, the delineation error is reduced from 1131.6 m to 68.7 m without any architectural modifications. These methodological advancements establish a framework for applying deep learning-based calving front segmentation to novel study sites, enabling calving front monitoring on a global scale.

翻译：在基准测试中，冰川崩解前缘划分的最先进模型已达到接近人类专家的性能水平。然而，当将其应用于真实场景中的新研究区域时，该模型的划分精度无法满足后续科学分析所需的崩解前缘产品要求。对于仅基于基准数据集训练的模型而言，该研究区域构成了分布外域。通过采用小样本域适应策略、融合空间静态先验知识，并在输入时间序列中引入夏季参考影像，划分误差从1131.6米降低至68.7米，且无需任何架构修改。这些方法学进展为将基于深度学习的崩解前缘分割技术应用于新研究区域建立了框架，为实现全球尺度的冰川崩解前缘监测提供了可能。