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.

中文翻译：

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使用深度卷积神经网络对真实世界监控摄像图像进行城市洪水范围分割和评估


本研究探索使用深度卷积神经网络（DCNN）对洪水图像进行语义分割。使用城市洪水图像数据集来训练两个基于 DCNN 的模型，并使用摄像机图像来测试模型与真实世界数据的应用。验证结果表明，两个模型提取的洪水范围的平均 F1 分数均超过 0.9。影响性能的因素包括镜面反射的静止水面、潮湿的路面和低照度。在测试中，暴风雨期间能见度降低以及监控摄像头上的雨滴是影响洪水范围分割的主要问题。高清网络摄像头可以作为一种替代工具，其模型可以根据其收集的数据进行训练。总之，基于 DCNN 的模型可以从城市洪水的摄像机图像中提取洪水范围。在本研究中确定的真实世界数据上使用这些模型所面临的挑战为未来的研究提供了机会。