"CNES ALCD Open water masks" is a reference dataset for water masks based on Sentinel-2 (L1C) images. This dataset generation has been funded by CNES under the SWOT-Downstream programme. 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 (water, 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 16 files (scenes) at 10m resolution for 110km x 110km size. The content of pixels of the scene files (geotiff) follows the following naming rule 0 = Non Water observation (as land, snow) 1 = Open Water observation 255 = no data (as clouds) Format of file names: T{tile}_{YYYMMDD}_{site}_{season}.tif where : tile = reference Sentinel 2 tile (Cesbio post), YYYYMMDD = date of Sentinel 2 acquisition, site = name of the site, season = summer, winter Example : T30TXQ_20180201_Bordeaux_winter.tif T30UXU_20180708_Bretagne_summer.tif