云在地球的大气运动中扮演着重要的角色，精准分割地基云图对天气预报精度的提升起着至关重要的作用。针对现有开源云图数据集数据质量不一、数据量低、拍摄角度不同等问题，通过人工标注和迁移学习的方法构建了带标签的标准地基云图数据集（Cloud-GT），利用颜色通道分量阈值分割法剔除太阳光干扰，并在此基础上提出了一种基于改进U2Net的地基云图分割技术。该模型在特征提取单元中引入了通道注意力模块和深度可分离卷积模块，在提高网络内部对地基云图有效特征提取的同时极大减少了网络模型参数。最后，将该方法与经典分割网络进行比较分析，实验结果表明，该方法的类别像素准确率、类别平均像素精度、平均交并比、交并比和F₁分别达到了84.03%、90.88%、84.13%、74.12%和89.59%，与U2Net、UNet和FCN相比，其效果有了明显的提升。可见该方法不仅极大地减少了模型的参数量，还有效提高了分割的精度，为实际应用提供了可能。

Clouds play a crucial role in the atmospheric dynamics of the Earth, and precise segmentation of ground-based cloud images is essential for improving the accuracy of weather forecasting. In response to issues such as varying data quality, low data volume, and different capture angles in existing open-source cloud image datasets, a labeled standard ground-based cloud image dataset（Cloud-GT） was constructed using manual annotation and transfer learning methods. The color channel component threshold segmentation method was employed to eliminate sunlight interference. Furthermore, an improved U2Net-based ground-based cloud image segmentation technique was proposed. The model introduced channel attention modules and depth-wise separable convolution modules in the feature extraction unit, which greatly reduces the network model parameters while improving the effective feature extraction of ground-based cloud maps within the network. Finally, comparing and analyzing the method with classical segmentation networks, experimental results indicated that the method achieved classification pixel accuracy, mean class pixel accuracy, average intersection over union, intersection over union, and F₁ score of 84.03%, 90.88%, 84.13%, 74.12%, and 89.59%, respectively. In comparison with U2Net, UNet, and FCN, the method demonstrated a significant improvement in performance. In conclusion, the method not only substantially reduced the model parameters but also effectively enhanced segmentation accuracy, which provides the possibility of practical application.