中国第二次冰川编目的部分数据用第一次冰川编目替代，这些数据集中分布在藏东南地区。该地区地形陡峭、气候恶劣，常年多云层覆盖，无法获取有效的光学影像，缺乏系统性的冰川调查。针对传统阈值分割方法受噪声影响大、标准Unet计算量大导致运行缓慢等问题，对Unet模型进行压缩，通过修改样本尺寸、卷积核数量和优化器等模型参数，提升模型训练效率以及冰川提取精度。利用冰川的极化特性和地形特征，选用45景ENVISAT ASAR影像和NASA DEM，基于Unet及其压缩网络进行深度学习，参考光学影像和其它辅助数据对误分和漏分的冰川逐个进行人工目视判读，完成了未更新编目的冰川边界提取及修正，并对属性进行了更新。结果表明：基于SAR影像和地形特征的深度学习可以有效识别云层覆盖区域的冰川。在第二次冰川编目未完成的地区，共有冰川8 374条，总面积5 622.65±303.58 km²，误差占总冰川面积的5.4%，整体呈退缩状态，冰川碎片化现象居多。该数据集更新了中国第二次冰川编目中的替代数据，可为探讨藏东南冰川变化和物质平衡等相关研究提供可靠的数据支撑。

Some of the data of the second Chinese glacier inventory（CGI2.0） are replaced by the first Chinese glacier inventory， and these data are concentrated in southeastern Tibetan Plateau. Where the terrain is steep， the climate is harsh， and it is covered by clouds all the year round. There is no systematic glacier survey due to the inability to obtain effective optical images. Aiming at the problems that the traditional threshold segmentation method is influenced by noise， and the standard Unet has a large amount of computation， which leads to slow operation， compressedUnet model is designed to improve model training efficiency and glacier extraction accuracy by modifying model parameters such as sample size， number of convolution kernel and optimizer. Using the polarization characteristics and topographic features of glaciers， 45-scene ENVISAT ASAR images and NASA DEM are selected to carry out deep learning based on Unet and compressed Unet. By referring to optical images and other auxiliary data， the misclassified and missed glaciers are visually interpreted one by one. Finally， the extraction and correction of the glacier boundaries without update are completed， and their attributes are updated. The results show that deep learning based on SAR images and topographic features can effectively identify glaciers in cloud-covered areas. In the areas where the CGI2.0 is not completed， there are 8 374 glaciers with a total area of 5 622.65±303.58 km²， and the error accounts for 5.4% of the total glacier area， most glaciers are retreating and fragmenting. The dataset updates the alternative data in CGI2.0， and provides reliable data support for related studies of glacier changes and mass balance in southeastern Tibetan Plateau.