Data of the paper " A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery " in Journal of Hydrology

The compressed files are the training datasets, the validation dataset, the lake prediction result and a checkpoint in the paper " A Transformer-based Method to Reduce Cloud Shadow Interference in Automatic Lake Water Surface Extraction from Sentinel-2 Imagery " in Journal of Hydrology . The “Training datasets. rar” includes 5 independent training datasets containing 0%, 1%, 2%, 3% and 4% cloud shadows respectively. Each training dataset includes ground truth folder and remote sensing image folder, namely "gt" and "image", which constitute 5000 sample pairs in total. The “Validation dataset.rar” is used to valid the accuracy of the model trained by the five training datasets, also including two folders "gt" and "image" , with a total of 220 sample pairs.The ”Prediction result.rar” is the lake prediction result in the Inner River Basin of the Tibetan Plateau using the model trained by the training dataset containing 4% cloud shadows. There are 112 tiles of images covering this region, so a total of 112 folders are included. And the “BestCheckpoint.ckpt” file is the model with the highest accuracy in the paper. For details, please refer to the paper: Yan, X ., Song, J., Liu, Y., Lu, S., Xu, Y., Ma, C., Zhu, Y., 2021. A Transformer-based method to reduce cloud shadow interference in automatic lake water surface extraction from Sentinel-2 imagery. J. Hydrol. 620, 129561. https://doi.org/10.1016/j.jhydrol.2023.129561