General description of the dataset The dataset for the flood binary segmentation task comprises 720 images consolidated from two sources: Mantoudi and Arthal trials. Mantoudi contributes with 338 images (198 train set, 140 test set) and Arthal with 382 images (230 train set, 152 test set), resulting in an approximate 60% – 40% training-validation distribution. The annotation masks are binary, where pixels are labeled as 0 for background and 1 for floodwater. If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following papers. P. Mentesidis, V. Mygdalis and I.Pitas, "Improve Real-time flood segmentation by encoding and distilling foreground information", IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA, 13-17 September, 2025. A. Gerontopoulos, D. Papaioannou, C. Papaioannidis and I.Pitas, "Real-Time Flood Water Segmentation with Deep Neural Networks", IEEE 25th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), Tromsø, Norway, pp. 85-91, 2025 Dataset Structure It is structured into distinct directories for each source (Mantoudi and Arthal), each containing standard train and validation splits with separate folders for images (.jpg) and labels (.png). Mora information about the dataset can be found here.