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| Analysis of inundation from social media based on integrated YOLOv5 and Mask-RCNN model |
Lingjia ZHANG( ),Xinlei ZHOU,Yueping XU,Yenming CHIANG*() |
| College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China |
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Abstract Due to the lack of empirical inundation data, a novel method was explored for extracting flood inundation information from social media uploads using social media and deep learning technologies, addressing the need for urban area inundation depth measurement data. Firstly, the image segmentation model based on the YOLOv5 and Mask-RCNN was constructed and the identification dataset of various key parts of vehicles was constructed. A novel method for extracting the inundation height during urban flooding events was proposed according to the model training results. By inputting inundation images, the prediction of submerged locations and depths in urban inundation was conducted. These predictions were then compared with the data obtained from the inundation recurrence model. A validation dataset was formed by conducting the simulated inundation experiments, to verify the feasibility of the proposed model. Results showed that the Nash efficiency coefficient of the proposed model was 0.98. Moreover, the social media images from the actual urban flooding during the "7·20" event in Zhengzhou City were used to verify the reliability of the proposed model. Results showed that the proposed method can provide an effective data source for the process of urban inundation.
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Received: 03 December 2023
Published: 30 August 2024
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| Fund: 浙江省重点研发计划资助项目（2021C03017） ；浙江省自然基金重点研究资助项目（LZ20E090001）. |
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Corresponding Authors:
Yenming CHIANG
E-mail: 22312102@zju.edu.cn;chiangym@zju.edu.cn
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基于YOLOv5和Mask-RCNN组合模型的社交媒体内涝灾害分析
由于缺少淹没实测数据，针对城市市区淹没深度测量数据需求，基于社交媒体与深度学习技术，探索根据社交媒体用户上传信息提取洪水淹没信息的新方法. 研发基于YOLOv5与Mask-RCNN组合模型的实例分割算法，制作轿车众多关键部位的识别数据集. 根据模型训练结果实现全新的市区淹没事件的淹没高度提取方法. 通过输入淹没图像对城市内涝中淹没点位与淹没深度进行预测，与淹没重演模型得到的数据进行比较. 基于模拟淹没实验来制作验证数据集，验证该方法的可行性. 结果表明，所研发的YOLOv5 与 Mask-RCNN组合模型的纳什效率系数为0.98. 使用郑州市“7·20”城市内涝的实际社交媒体图像进行可靠性验证. 结果表明，所提方法能够为城市市区内涝淹没过程提供有效数据来源.
关键词：
城市内涝,
社交媒体,
YOLOv5,
Mask-RCNN,
水深提取,
图像识别
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