CESBIO ALCD Snow masks

"CNES ALCD Snow masks" is a reference dataset for snow masks based on Sentinel-2 (L1C) images. This dataset has been generated with the Active Learning for Cloud Detection (ALCD) software developed by CNES/Cesbio, that enables to generate any kind of reference mask using satellite images. This procedure involves between 1 or 2 hours of work to generate each reference image : create reference points on the image ( land, cloud, snow...) manually, do the training (based on Random Forest of OTB) and prediction with ALCD, add new reference points for the most problematic areas, repeat new training/predictions as many times as necessary (usually 3-5 iterations), and finally, do a manual correction of persistent errors. The dataset contains 10 folders each containing 1 geotiff file (scene) at 20m resolution for 110kmx110km size, 1 quicklook of the scene before and 1 quicklook after classification. The content of pixels of the geotiff files follows the following naming rule : 0 = nodata; 3 = Cloud; 5 = Land; 7 = Snow Format of folder names: {location}_{tile}_{YYYMMDD} Where tile = reference Sentinel 2 tile (Cesbio post) , YYYYMMDD = date of Sentinel 2 acquisition, location = name of the site (ex: "PYR" = Pyrenees) Example: PYR_31TCH_20191117"CNES ALCD雪掩膜数据集"是一款基于Sentinel-2（L1C）影像构建的雪覆盖掩膜参考数据集。 该数据集由法国国家空间研究中心（CNES）与Cesbio联合开发的主动学习云检测（Active Learning for Cloud Detection, ALCD）软件生成，该工具可依托卫星影像生成各类参考掩膜产品。 单幅参考影像的生成流程耗时1至2小时，具体步骤包括：首先在影像上手动标注参考点（涵盖陆地、云、雪等地物类别）；基于OTB的随机森林模型开展训练，并通过ALCD工具完成预测；针对问题突出的区域补充新的参考点，按需重复训练与预测流程（通常需3至5轮迭代）；最终对持续存在的错误进行人工修正。 本数据集共包含10个文件夹，每个文件夹内均存储1幅分辨率为20m、尺寸为110km×110km的GeoTIFF影像（场景），以及分类前、分类后的场景快速预览图（quicklook）各1份。 GeoTIFF影像的像素值遵循以下命名规则： 0 = 无数据（nodata）；3 = 云；5 = 陆地；7 = 雪 文件夹命名格式为：{location}_{tile}_{YYYMMDD} 其中，tile为参考Sentinel-2瓦片（Cesbio标注后缀），YYYMMDD为Sentinel-2影像的获取日期，location为研究站点名称（例如"PYR"代表比利牛斯山脉）。 示例：PYR_31TCH_20191117