Maslov Konstantin A, Persello Claudio, Schellenberger Thomas, Stein Alfred
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Overijssel, The Netherlands.
Department of Geosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, Østlandet, Norway.
Nat Commun. 2025 Jan 2;16(1):43. doi: 10.1038/s41467-024-54956-x.
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.
准确的全球冰川测绘对于理解气候变化影响至关重要。尽管其很重要，但全球尺度的自动化冰川测绘在很大程度上仍未得到充分探索。在此，我们填补这一空白，提出了冰川视觉Transformer - U型网络（GlaViTU），这是一种卷积 - Transformer深度学习模型，以及五种使用公开卫星图像进行多时间尺度全球尺度冰川测绘的策略。对空间、时间和跨传感器泛化的评估表明，我们最佳的策略在大多数情况下，在以前未观测到的图像上实现了交并比>0.85，对于像亚洲高山地区这样富含碎屑的区域，交并比降至>0.75，而对于以干净冰为主的区域，交并比增加到>0.90。在面积和距离偏差方面针对人类专家不确定性进行的比较验证突出了GlaViTU的性能，接近或匹配专家级的描绘。添加合成孔径雷达数据，即后向散射和干涉相干性，在所有可用数据的区域提高了准确性。报告了冰川范围的校准置信度，使预测更加可靠且可解释。我们还发布了一个覆盖全球9%冰川的基准数据集。我们的结果支持了自动化多时间尺度和全球冰川测绘的努力。
