发表机构
Towson University; University of Maryland, Baltimore County（陶森大学; 马里兰大学巴尔的摩县分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在CaFFe数据集上比较直接预测与区域引导的崩解前缘提取方法，发现区域标签虽含边界信息，但模型预测区域提取的前缘仍弱，区域分割不能替代前缘级评估。
AI 中文摘要
从合成孔径雷达影像中自动描绘崩解前缘具有挑战性，因为前缘是冰川冰、海洋以及周围岩石或地形之间的一条细且往往模糊的边界。CAlving Fronts and where to Find thEm (CaFFe)数据集提供了二值崩解前缘掩膜和更广泛的语义区域掩膜，使得研究区域级监督能否支持前缘恢复成为可能。在本文中，我们在相同的边界框裁剪CaFFe设置下，使用U-Net、DeepLabV3+和SegFormer-B0比较了直接前缘预测与区域引导的前缘提取。在直接设置中，模型预测二值崩解前缘掩膜。在区域引导设置中，模型首先预测四个语义区域类别，然后从预测的冰川-海洋边界中提取前缘。我们评估了区域级和前缘级性能，包括真实区域边界检查，并检验了针对传感器特定和冰川特定子集的轻量级测试时自适应。结果表明，区域标签包含有用的前缘边界信息：从真实区域提取前缘给出了最低的平均距离误差。然而，从模型预测区域提取的前缘仍然较弱，即使区域分割得分中等。测试时自适应也没有持续改善区域引导的前缘恢复。这些结果表明，区域分割性能不应被视为前缘级评估的替代品，有效利用区域标签可能需要边界感知训练、标签融合或显式前缘监督。
英文摘要
Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary between glacier ice, ocean, and surrounding rock or terrain. The CAlving Fronts and where to Find thEm (CaFFe) dataset provides both binary calving-front masks and broader semantic zone masks, making it possible to study whether zone-level supervision can support front recovery. In this paper, we compare direct front prediction with zone-guided front extraction using U-Net, DeepLabV3+, and SegFormer-B0 under the same bounding-box-cropped CaFFe setting. In the direct setting, models predict the binary calving-front mask. In the zone-guided setting, the model first predicts four semantic zone classes, and the front is then extracted from the predicted glacier-ocean boundary. We evaluate both zone-level and front-level performance, include a ground-truth-zone boundary check, and examine lightweight test-time adaptation on sensor-specific and glacier-specific subsets. The results show that zone labels contain useful front-boundary information: extracting the front from ground-truth zones gives the lowest mean distance error. However, fronts extracted from model-predicted zones remain weak, even when zone segmentation scores are moderate. Test-time adaptation also does not consistently improve zone-guided front recovery. These results indicate that zone segmentation performance should not be treated as a substitute for front-level evaluation and that effective use of zone labels may require boundary-aware training, label fusion, or explicit front supervision.
CommentsAccepted in ICMLA 2026 as short paper. 4 pages, 1 figure
