Flood Master Dataset

General description of the dataset

This dataset comprises flood-related images collected from several publicly available datasets, as well as frames extracted and annotated from related videos. The training and validation sets were constructed using the following sources: “Flood Area Segmentation,” “Water Dataset,” and “Roadway Flooding Image Dataset.” For the first two sources, the corresponding binary masks were normalized to values in {0,1}, while the third source was retained in its original normalized form. The test set consists of video frames extracted from real flooding scenarios in Greece and Italy, which were annotated for segmentation purposes. Specifically, the Greek and Italian videos contributed 567 and 1,406 frames, respectively. 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

The dataset is organized into separate folders for the training, validation, and test sets, each containing the corresponding annotations and a CSV file specifying the image paths, annotation paths, and the source of each image. The training–validation split was performed using a 3:1 ratio. Finally, the URLs of the original data sources are provided in the “sources.csv” file.

Details on acquiring the dataset can be found here.