Accurate global glacier mapping is critical for understanding climate change impacts. It is challenged by glacier diversity, difficult-to-classify debris and big data processing. Here we 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. Additionally, 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.

准确的全球冰川绘图对于理解气候变化的影响至关重要，但冰川的多样性、难以分类的残渣和大数据处理提出了挑战。本论文提出了一种卷积-变换器的深度学习模型(GlaViTU)和五种策略，利用开放卫星图像进行多时期的全球冰川绘图。评估空间、时间和跨传感器的泛化性表明，我们最佳策略在大多数情况下，在先前未观测到的图像上实现了交并比大于0.85，对于残渣丰富的地区如高山亚洲，这个值下降到了大于0.75，而对于以干净冰为主的地区，这个值增加到了大于0.90。此外，添加合成孔径雷达数据，即回波和干涉相干度，提高了所有有可用数据的地区的准确性。报告了冰川扩展的校准置信度，使预测更加可靠和可解释。我们还发布了覆盖全球9%的基准数据集。我们的结果支持自动化多时期和全球冰川绘图的努力。

利用深度学习和开放地球观测数据实现全球冰川绘制