The Sentinel-2 satellite constellation produces high-resolution multispectral data, covering the entire Earth's land surface every five days. However, the use of this data is significantly impacted by the presence of pixels affected by cloud and shadow. Precise and efficient cloud and cloud shadow masking methods are required for the automated use of this data. Here, we present CloudS2Mask, a new and open-source Python deep-learning library to detect cloud and cloud shadows in Sentinel-2 data accurately and efficiently. CloudS2Mask is trained on multiple large global datasets including CloudSEN12, KappaSet, Sentinel-2 Cloud Mask Catalogue, Sentinel-2 reference cloud masks generated by an active learning method and a custom CloudS2Mask dataset. Using techniques such as progressive resizing, hard fine-tuning, and transfer learning, CloudS2Mask outperforms published models when evaluated on both the CloudSEN12 and the PixBox datasets in terms of classification accuracy. Compared to the next best-published results, CloudS2Mask improves Balanced Overall Accuracy metrics on the CloudSEN12 dataset by 1.2% for cloud, 2.8% for cloud shadow and 1.1% for clear classification. CloudS2Mask provides a range of customisable options combined with novel pre-processing and post-processing approaches to meet task-specific needs, enabling mean scene processing speeds up to 44 times faster than current state-of-the-art methods. The application of novel techniques in CloudS2Mask, combined with its accuracy and processing speed, underscores its potential to streamline the automated cloud and cloud shadow detection of Sentinel-2 data.

中文翻译：

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CloudS2Mask：一种新颖的深度学习方法，用于改进 Sentinel-2 图像中的云和云阴影遮蔽

Sentinel-2 卫星星座每五天产生一次覆盖整个地球陆地表面的高分辨率多光谱数据。然而，这些数据的使用受到受云和阴影影响的像素的存在的显着影响。自动使用这些数据需要精确、高效的云和云阴影遮罩方法。在这里，我们推出 CloudS2Mask，这是一个新的开源 Python 深度学习库，用于准确高效地检测 Sentinel-2 数据中的云和云阴影。 CloudS2Mask 在多个大型全球数据集上进行训练，包括 CloudSEN12、KappaSet、Sentinel-2 云掩模目录、主动学习方法生成的 Sentinel-2 参考云掩模和自定义 CloudS2Mask 数据集。使用渐进调整大小、硬微调和迁移学习等技术，在 CloudSEN12 和 PixBox 数据集上评估分类准确性时，CloudS2Mask 的性能优于已发布的模型。与排名第二的最佳发布结果相比，CloudS2Mask 将 CloudSEN12 数据集的平衡总体准确度指标（云）提高了 1.2%，云阴影提高了 2.8%，清晰分类提高了 1.1%。 CloudS2Mask 提供一系列可定制选项，结合新颖的预处理和后处理方法，以满足特定任务的需求，使平均场景处理速度比当前最先进的方法快 44 倍。 CloudS2Mask 中新技术的应用，结合其准确性和处理速度，凸显了其简化 Sentinel-2 数据的自动化云和云阴影检测的潜力。