Accurate global glacier mapping is critical for understanding climate
change impacts. Despite its importance, automated glacier mapping at a global
scale remains largely unexplored. Here we address this gap and propose
Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep
learning model, and five strategies for multitemporal global-scale glacier
mapping using open satellite imagery. Assessing the spatial, temporal and
cross-sensor generalisation shows that our best strategy achieves intersection
over union >0.85 on previously
unobserved images in most cases, which drops to
>0.75 for debris-rich areas such as High-Mountain Asia and increases
to >0.90 for regions dominated by
clean ice. A comparative validation against human expert uncertainties in terms
of area and distance deviations underscores GlaViTU performance, approaching or
matching expert-level delineation. Adding synthetic aperture radar data,
namely, backscatter and interferometric coherence, increases the accuracy in
all regions where available. The calibrated confidence for glacier extents is
reported making the predictions more reliable and interpretable. We also
release a benchmark dataset that covers 9% of glaciers worldwide. Our results
support efforts towards automated multitemporal and global glacier mapping.
准确的全球冰川制图对理解气候变化的影响至关重要。然而，全球范围内的自动冰川制图仍鲜有探索。为填补这一空白，我们提出了一个名为 Glacier-VisionTransformer-U-Net (GlaViTU) 的卷积-Transformer深度学习模型，以及五种多时相全球冰川制图策略，利用公开的卫星影像。通过对空间、时间和跨传感器的泛化能力评估表明，我们的最佳策略在大多数情况下实现了交并比（IoU）>0.85，而在诸如亚洲高山区等富含表碛覆盖区域，这一指标下降到 >0.75；在以清洁冰为主的区域则提升到>0.90。与人类专家在面积和距离偏差上的不确定性进行比较验证显示，GlaViTU 的性能接近或达到专家级划分水平。加入合成孔径雷达数据（SAR）（包括后向散射和干涉相干性）后，所有可用区域的精度均有所提高。我们还对冰川范围预测进行了置信度校准，使结果更可靠且更具解释性。同时，我们发布了一个覆盖全球9%冰川的基准数据集。我们的研究成果支持实现自动化、多时相和全球范围的冰川制图工作。
