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Social Sensing in Disaster City Digital Twin: Integrated Textual-Visual-Geo Framework for Situational Awareness during Built Environment Disruptions

机译:灾难城市数字双胞胎的社会传感:构建环境中断期间情境意识的综合文本视觉地理框架

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This paper proposed and tested an integrated textual-visual-geo framework to enhance social sensing techniques in smart city digital twins in the context of disasters. Effective and efficient disaster response and recovery require reliable situational awareness regarding infrastructure disruptions and their societal impacts. Due to the rapid unfolding and evolution of events in disasters and emergencies, typical data sensing techniques (such as remote sensing and satellite images) are not sufficient to gain reliable situational awareness about disruptions that affect communities at a local scale. Social sensing enables gathering and analyzing massive user-generated data from various sources (social media, in particular) to monitor unfolding of localized events such as infrastructure disruptions and community needs. To advance social sensing methods and their integration into digital twins of cities, this study proposes an integrated framework for detecting infrastructure disruptions based on three information elements embedded in social media content: images, texts, and geo-maps. The framework consists of three main methods: a graph-based approach for detecting critical tweets, an image-ranking algorithm for selecting important images, and a kernel density estimate for estimating the geographical scales of the disruptions. The application of the proposed framework was demonstrated in a case study of water release from flood control reservoirs in Houston during Hurricane Harvey in 2017. The findings illustrate the capabilities of the proposed framework for capturing the critical situational information and interpreting the results for situational awareness and disruption response. The proposed framework can enhance integration of social sensing elements into smart city digital twins in the context of disasters. Accordingly, the proposed framework can improve the ability of community members, volunteer responders, residents, and other stakeholders in coping with built environment disruptions in disasters.
机译:本文提出并测试了一个集成的文本视觉地理框架,以增强智能城市数字双胞胎的社会传感技术在灾难的背景下。有效和高效的灾害反应和恢复需要有关基础设施中断及其社会影响的可靠情境意识。由于灾害和紧急情况的事件的快速展开和演变,典型的数据感测技术(例如遥感和卫星图像)不足以获得对影响局部规模影响社区的中断的可靠情境意识。社交传感使得能够收集和分析来自各种来源的大规模用户生成的数据(特别是社交媒体,特别是社交媒体)来监视诸如基础架构中断和社区需求之类的本地化事件的展开。为了推进社会传感方法及其融入城市数字双胞胎的融合,本研究提出了一种综合框架,用于根据社交媒体内容嵌入的三个信息元素来检测基础设施中断:图像,文本和地理映射。该框架由三种主要方法组成:一种基于图形的方法,用于检测关键推文,用于选择重要图像的图像排名算法,以及用于估计中断的地理标度的内核密度估计。拟议框架的应用是在2017年飓风Harvey休斯顿洪水控制储层的水释放的情况下进行了证明。该研究结果说明了拟议框架的能力,用于捕获批判性情境信息并解释态势意识的结果和解释结果中断响应。拟议的框架可以在灾害背景下加强社会传感元素进入智能城市数字双胞胎的整合。因此,拟议的框架可以提高社区成员,志愿响应者,居民和其他利益攸关方应对灾害的建造环境中断的能力。

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  • 来源
    《Journal of Management in Engineering》 |2020年第3期|04020002.1-04020002.13|共13页
  • 作者单位

    Zachry Dept. of Civil and Environmental Engineering Texas A&M Univ. 400 Bizzell St. College Station TX 77843;

    Undergraduate Research Assistant Dept. of Computer Science and En-gineering Texas A&M Univ. 400 Bizzell St. College Station TX 77843;

    Zachry Dept. of Civil and Environmental Engi-neering Texas A&M Univ. 400 Bizzell St. College Station TX 77843;

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