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| Created: | Dec 06, 2023 at 4:39 p.m. | |
| Last updated: | Feb 20, 2024 at 6:23 p.m. | |
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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.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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