YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Management in Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Management in Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

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

    Source: Journal of Management in Engineering:;2020:;Volume ( 036 ):;issue: 003
    Author:
    Chao Fan
    ,
    Yucheng Jiang
    ,
    Ali Mostafavi
    DOI: 10.1061/(ASCE)ME.1943-5479.0000745
    Publisher: ASCE
    Abstract: 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.
    • Download: (1.673Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

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

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4266063
    Collections
    • Journal of Management in Engineering

    Show full item record

    contributor authorChao Fan
    contributor authorYucheng Jiang
    contributor authorAli Mostafavi
    date accessioned2022-01-30T19:50:11Z
    date available2022-01-30T19:50:11Z
    date issued2020
    identifier other%28ASCE%29ME.1943-5479.0000745.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4266063
    description abstractThis 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.
    publisherASCE
    titleSocial Sensing in Disaster City Digital Twin: Integrated Textual–Visual–Geo Framework for Situational Awareness during Built Environment Disruptions
    typeJournal Paper
    journal volume36
    journal issue3
    journal titleJournal of Management in Engineering
    identifier doi10.1061/(ASCE)ME.1943-5479.0000745
    page04020002
    treeJournal of Management in Engineering:;2020:;Volume ( 036 ):;issue: 003
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian