Feasibility study of urban flood mapping using traffic signs for route optimization
Feasibility study of urban flood mapping using traffic signs for route optimization
复制标题
利用交通标志进行城市洪水测绘以优化路线的可行性研究
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Behzadan
中科院分区：
文献类型：
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作者：
B. Kharazi;Diya Li;Zhe Zhang;A. Behzadan
水事件是世界上最频繁和最昂贵的气候灾害。在美国，据估计，生活在沿海地区的1.27亿人面临飓风或洪水造成的严重房屋破坏的风险。在洪水应急管理中，及时有效的空间决策和智能路由依赖于洪水深度信息在一个精细的时空尺度。在本文中，众包被用来收集淹没的停车标志的照片，并将每张照片与洪水前在同一位置拍摄的照片配对。然后使用深度神经网络和图像处理来分析每个照片对，以估计照片位置的洪水深度。生成的逐点深度数据被转换为洪水淹没图，并由A* 搜索算法用于确定连接感兴趣点的最佳无洪水路径。结果提供了重要的信息，救援队和疏散人员，使有效的寻路在洪水事件。
Water events are the most frequent and costliest climate disasters around the world. In the U.S., an estimated 127 million people who live in coastal areas are at risk of substantial home damage from hurricanes or flooding. In flood emergency management, timely and effective spatial decision-making and intelligent routing depend on flood depth information at a fine spatiotemporal scale. In this paper, crowdsourcing is utilized to collect photos of submerged stop signs, and pair each photo with a pre-flood photo taken at the same location. Each photo pair is then analyzed using deep neural network and image processing to estimate the depth of floodwater in the location of the photo. Generated point-by-point depth data is converted to a flood inundation map and used by an A* search algorithm to determine an optimal flood-free path connecting points of interest. Results provide crucial information to rescue teams and evacuees by enabling effective wayfinding during flooding events.