CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Description CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups: - clear (0%) - low-cloudy (1% - 25%) - almost clear (25% - 45%) - mid-cloudy (45% - 65%) - cloudy (65% >) An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group: 2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group). 2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group). 5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group). For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no annotation dataset is generated automatically, with manual annotation only in the clear image patch. The dataset is already available here: https://shorturl.at/cgjtz. Check out our website https://cloudsen12.github.io/ for examples of how to download the dataset via STAC.CloudSEN12是一款面向云语义理解的大型数据集，共包含9880个感兴趣区域（Regions of Interest，ROIs）。每个ROI包含5幅采集自不同日期的5090×5090米图像块（Image Patches，IPs）；我们通过人工筛选图像，确保单个ROI内的每幅IP均对应以下云量分组之一：无云（0%）、少云（1%~25%）、近乎无云（25%~45%）、中云（45%~65%）、多云（>65%）。图像块是CloudSEN12的核心单元，每幅IP均包含哨兵2号（Sentinel-2）1C级与2A级光学影像、哨兵1号（Sentinel-1）合成孔径雷达（Synthetic Aperture Radar，SAR）数据、数字高程模型、地表水发生数据、土地覆盖分类数据，以及8种前沿云检测算法生成的云掩膜结果。此外，为支持标准监督、弱监督以及自/半监督学习流程，CloudSEN12提供三种不同类型的人工标注数据：高质量标注、涂鸦标注与无标注。据此，每个ROI会被随机分配至以下三个标注组别之一：2000个ROI采用像素级高质量标注，单组平均标注时长为150分钟（高质量组）；2000个ROI采用涂鸦级标注，单组标注时长为15分钟（涂鸦组）；5880个ROI仅在无云（0%云量）的图像块上进行标注（无标注组）。对于高质量标注，我们采用了图像分割智能系统Intelligence foR Image Segmentation（IRIS）的主动学习技术，该系统结合了人工图像解译与机器学习流程。涂鸦标注则通过IRIS完成真值像素绘制，但未借助机器学习支持。无标注数据集则为自动生成，仅在无云图像块上进行人工标注校验。该数据集已可通过以下链接获取：https://shorturl.at/cgjtz。如需了解如何通过时空资产目录（SpatioTemporal Asset Catalog，STAC）下载数据集，请访问我们的官方网站https://cloudsen12.github.io/查看操作示例。