Cloud detection (CD) with deep learning (DL) algorithms has been greatly developed in the applications involving the predictions of extreme weather and climate. In this review, the different conventional CD methods based on threshold, time differentiation, machine learning, and the intelligent algorithms including convolution neural networks (CNN), simple linear iterative clustering (SLIC), and semantic segmentation algorithms (SSAs) are introduced in detail, and, especially, the majority of CD publications employing the advanced and prevalent DL algorithms during the last decade are summarized and analyzed. First, in terms of the detection for different types of clouds, we meticulously compare the labels, scenarios and volumes of three popular CD datasets and put forward further the constructive recommendations about the cloud images selection, multi-bands images preprocessing, and truth labels combination for creating similar datasets. Subsequently, the structures, detection accuracies, and operating speeds of several different CD network models comprising the fully convolutional neural networks (FCNs), U-Net, SegNet, pyramid scene parsing network (PSPNet), as well as the associated derivatives are conducted elaborately to explore the comprehensively optimal performance for CD. In addition, aiming at expanding the applications in the resource-limited space-borne environment, we conclude the mainstream compression strategies of a number of different lightweight networks. Finally, the various limitations constraining the performance of the existing state-of-the-art DL CD methods and the corresponding development tendency are presented, which, expectantly, could be referential for the following researches.

具有深度学习（DL）算法的云检测（CD）在涉及极端天气和气候预测的应用中得到了极大的发展。在这篇综述中，详细介绍了基于阈值，时间微分，机器学习以及包括卷积神经网络（CNN），简单线性迭代聚类（SLIC）和语义分割算法（SSA）在内的智能算法的不同常规CD方法。尤其是最近十年中采用先进且流行的DL算法的大多数CD出版物都得到了总结和分析。首先，在检测不同类型的云时，我们会仔细比较三个流行CD数据集的标签，场景和容量，并进一步提出有关云图像选择的建设性建议，多波段图像预处理和真相标签组合以创建相似的数据集。随后，详细地进行了包括完全卷积神经网络（FCN），U-Net，SegNet，金字塔场景解析网络（PSPNet）以及相关派生工具在内的几种不同CD网络模型的结构，检测精度和运行速度的讨论。探索CD的综合最佳性能。此外，为了在资源有限的星载环境中扩展应用程序，我们总结了许多不同轻量级网络的主流压缩策略。最后，提出了各种限制现有技术水平的DL CD方法的性能以及相应的发展趋势，这些方法有望