用少量新数据和时间参考，让冰川断裂线分割模型快速适应新地点。
Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation
- 结合静态地理先验与夏季参考图像，实现少样本域自适应。
- 在新站点上分割误差从1131.6米降至68.7米。
- 适合需要快速部署到全球新冰川监测点的研究者使用。
基准测试中，当前最优的冰川断裂线分割模型已接近人工水平。但在真实场景下应用于新研究区域时，其分割精度不足以支持后续科学分析。该区域属于仅在基准数据集上训练的模型所不涵盖的分布外域。通过采用少样本域自适应策略，引入空间静态先验知识，并在输入时序中包含夏季参考图像，分割误差从1131.6米降至68.7米，且无需修改网络结构。这些方法为将基于深度学习的断裂线分割技术推广至新研究区域提供了框架，有望实现全球范围的冰川断裂线动态监测。
原文摘要 · Abstract (English)
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.
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