Deep Fmask Dataset: Labeled dataset for Cloud, Shadow, Clear-Sky Land, Snow and Water Segmentation of Sentinel-2 Images over Snow and Ice Covered Regions - Kamal Gopikrishnan Nambiar; Veniamin I Morgenshtern; Philipp Hochreuther; Thorsten Seehaus; Matthias Holger Braun (PANGAEA, 2022)
We present our dataset containing images with labeled polygons, annotated over Sentinel-2 L1C imagery from snow and ice-covered regions. We use similar labels as the Fmask cloud detection algorithm, i.e., clear-sky land, cloud, shadow, snow, and water. We annotated the labels manually using the QGIS software. The dataset consists of 45 scenes divided into validation (22 scenes) and test datasets (23 scenes). The source images were captured by the satellite between October 2019 and December 2020. We provide the list of extquotesingle.SAFE extquotesingle filenames containing the satellite imagery and these files can be downloaded from the Copernicus Open Access Hub. The dataset can be used to test and benchmark deep neural networks for the task of cloud, shadow, and snow segmentation.