The machine learning approach has shown its state‐of‐the‐art ability to handle segmentation and detection tasks. It is increasingly employed to extract patterns and spatiotemporal features from the ever‐increasing stream of Earth system data. However, there is still a significant challenge, which is the generalization capability of the model on cloud images in different types and weather conditions. After studying several popular methods, we propose a semantic segmentation neural network for cloud segmentation. It extracts features learned by source and target domains in an end‐to‐end behavior, which can address the problem of significant lack of labels in the observed cloud image data. It is further evaluated on the Singapore Whole Sky Image Segmentation (SWIMSEG) dataset by using Mean Intersection‐over‐Union, recall, F‐score, and accuracy matrices. The scores of these matrices are 86%, 97%, 92%, and 96%, which prove that it has excellent efficiency and robustness. Most importantly, a new benchmark based on the SWIMSEG dataset for the task of cloud segmentation is introduced. The others, BENCHMARK, Cirrus Cumulus Stratus Nimbus are evaluated through the model trained from the SWIMSEG dataset by way of visualization.

中文翻译：

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三种基于地面的云数据集的语义分割的有效解决方案

机器学习方法显示了其处理分段和检测任务的最先进能力。它越来越多地用于从不断增长的地球系统数据流中提取模式和时空特征。但是，仍然存在重大挑战，那就是模型在不同类型和天气条件下对云图像的泛化能力。在研究了几种流行的方法之后，我们提出了一种用于云分割的语义分割神经网络。它以端到端的方式提取源域和目标域学习的功能，可以解决观察到的云图像数据中标签严重不足的问题。通过使用均值交集，召回率，F得分，新加坡总天空图像分割（SWIMSEG）数据集对它进行进一步评估，和准确性矩阵。这些矩阵的分数分别为86％，97％，92％和96％，证明其具有出色的效率和鲁棒性。最重要的是，引入了基于SWIMSEG数据集的新基准，用于云分割任务。通过可视化，通过从SWIMSEG数据集训练的模型评估了其他的BENCHMARK，Cirrus Cumulus Stratus Nimbus。