Document Type
Article
Publication Date
2024
DOI
10.1016/j.envsoft.2023.105939
Publication Title
Environmental Modelling & Software
Volume
173
Pages
105939 (1-11)
Abstract
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.
Rights
© 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: https://www.hydroshare.org/resource/24866122a6ee456c8f7c80aa87a9abcb."
ORCID
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. https://doi.org/10.1016/j.envsoft.2023.105939
Repository Citation
Wang, Yidi; Shen, Yawen; Salahshour, Behrouz; Cetin, Mecit; Iftekharuddin, Khan; Tahvildari, Navid; Huang, Guoping; Harris, Devin K.; Ampofo, Kwame; and Goodall, Jonathan L., "Urban Flood Extent Segmentation and Evaluation from Real-World Surveillance Camera Images Using Deep Convolutional Neural Network" (2024). Civil & Environmental Engineering Faculty Publications. 80.
https://digitalcommons.odu.edu/cee_fac_pubs/80
Included in
Artificial Intelligence and Robotics Commons, Civil and Environmental Engineering Commons, Electrical and Computer Engineering Commons