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Buy them out before they are built: evaluating the proactive acquisition of vacant land in flood-prone areas.在它们建成之前买下它们:评估在易受洪水影响地区主动收购闲置土地的行为。
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Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image.基于局部全卷积神经网络与 YOLO v5 算法的遥感图像小目标检测应用
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飓风风暴潮对城市基础设施影响的三维可视化

3D Visualization of Hurricane Storm Surge Impact on Urban Infrastructure.

作者信息

Ye Xinyue, Li Shoujia, Gao Ge, Retchless David, Cai Zhenhang, Newman Galen, Du Jiaxin, Duffield Nick

机构信息

Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, TX, USA.

Department of Marine and Coastal Environmental Science, Texas A&M University, Galveston, TX, USA.

出版信息

Urban Inform. 2024 Dec;3. doi: 10.1007/s44212-024-00043-x. Epub 2024 May 8.

DOI:10.1007/s44212-024-00043-x
PMID:40831753
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12360663/
Abstract

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

摘要

随着气候变化加剧,降雨事件愈发严重,全球沿海城市正遭受重大生命和财产损失。城市暴雨洪水模拟成为防洪救灾中日益关键的组成部分,有助于为应急管理提供明智决策。数据的海量以及三维计算的复杂性使得可视化城市洪水对基础设施的影响颇具挑战。本研究对德克萨斯州加尔维斯顿住宅遭受飓风风暴潮洪水的影响进行了三维可视化展示,阐明了不同风暴条件下对各建筑物和道路的影响。我们利用目标检测来确定房屋门的位置,将门的淹没情况作为衡量潜在洪水损害的指标。在基于地理信息系统的框架内,我们对不同风暴强度下住宅的受损范围进行建模。我们的研究实现了三个核心目标:1)估算不同风暴条件下房屋的风暴淹没水平;2)评估一楼海拔高度,将房屋损坏分为三个不同类别;3)通过可视化展示拟议的堤坝在保护加尔维斯顿岛免受未来风暴潮和洪水事件影响方面的成效。