Document Type


Publication Date




Publication Title

Environmental Modelling & Software




105939 (1-11)


This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. High-definition web cameras can be an alternative tool with the models trained on the data it collected. In conclusion, DCNN-based models can extract flood extent from camera images of urban flooding. The challenges with using these models on real-world data identified through this research present opportunities for future research.


© 2024 The Authors.

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

Data Availability

Article states: "The datasets in this study are available in Hydrology Share at the following address:"


0000-0003-2134-2133 (Salahshour), 0000-0003-2003-9343 (Cetin), 0000-0001-8316-4163 (Iftekharuddin), 0000-0001-9922-129X (Tahvildari)

Original Publication Citation

Wang, Y., Shen, Y., Salahshour, B., Cetin, M., Iftekharuddin, K., Tahvildari, N., Huang, G., Harris, D. K., Ampofo, K., & Goodall, J. L. (2024). Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network. Environmental Modelling and Software, 173, 1-11, Article 105939.