Virtual Testbeds for Community Resilience Analysis: State-of-the-Art Review, Consensus Study, and Recommendations

Enderami, S. Amin; Mazumder, Ram K.; Dumler, Meredith; Sutley, Elaina J.

doi:10.1061/(asce)nh.1527-6996.0000582

Cited by 11 publications

(3 citation statements)

References 102 publications

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“…When it comes to building identification, the necessity for deep learning arises only in the absence of geotagged building data; however, it becomes redundant when such data are readily available. Given that governments and counties usually maintain comprehensive data sets, and testbeds provide accessible data for research purposes (Amin Enderami et al., 2022), it becomes more efficient to leverage such existing data for extracting building subimages. Employing highly accurate geotagged data ensures that the building identification process is virtually error free and allows the deep learning model to focus exclusively on the damage classification task.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, a gap lies in the transition from technical advancements in machine learning classification to the practical implementation of automated systems for postdisaster emergency response. Another gap pertains to the underutilization of GIS to identify buildings despite the heavy utilization of geotagged building databases in the risk assessment field (Amin Enderami et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

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Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

Braik,

Koliou

2024

Computer aided Civil Eng

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Efficient and accurate building damage assessment is crucial for effective emergency response and resource allocation following natural hazards. However, traditional methods are often time consuming and labor intensive. Recent advancements in remote sensing and artificial intelligence (AI) have made it possible to automate the damage assessment process, and previous studies have made notable progress in machine learning classification. However, the application in postdisaster emergency response requires an end‐to‐end model that starts with satellite imagery as input and automates the generation of large‐scale damage maps as output, which was rarely the focus of previous studies. Addressing this gap, this study integrates satellite imagery, Geographic Information Systems (GIS), and deep learning. This enables the creation of comprehensive, large‐scale building damage assessment maps, providing valuable insights into the extent and spatial variation of damage. The effectiveness of this methodology is demonstrated in Galveston County following Hurricane Ike, where the classification of a large ensemble of buildings was automated using deep learning models trained on the xBD data set. The results showed that utilizing GIS can automate the extraction of subimages with high accuracy, while fine‐tuning can enhance the robustness of the damage classification to generate highly accurate large‐scale damage maps. Those damage maps were validated against historical reports.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

Braik,

Koliou

2024

Computer aided Civil Eng

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“…The Lumberton Testbed is a virtual community resilience testbed that has been developed using public, secondary data and based on observations from a longitudinal field study on the impacts and recovery process of the community [26][27][28][29]. A community resilience testbed is a virtual "environment with enough supporting architecture and metadata to be representative of one or more systems such that the testbed can be used to (a) design experiments, (b) examine model or system integration, and (c) test theories" [30]. The field study also captured data on school and business functionality at different points in time, including operational status and customer loss, which provides information needed for calculating the accessibility metrics proposed here.…”

Section: Illustrative Example Using the Lumberton Testbedmentioning

confidence: 99%

Measuring Post-Disaster Accessibility to Essential Goods and Services: Proximity, Availability, Adequacy, and Acceptability Dimensions

Enderami,

Sutley,

Helgeson

et al. 2024

Preprint

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Rapid restoration of access to essential goods and services has long been regarded as paramount for community recovery. Yet, there remains ambiguity in how access should be defined, measured, or operationalized. Defining accessibility as the ability to use available goods and services with a reasonable effort and cost requires evaluating it across six dimensions (proximity, availability, adequacy, acceptability, affordability, and awareness) while considering the perspective of both users and providers in the evaluation. But common distance-based metrics that focus solely on physical access and travel time often fall short of fully capturing these requirements, overlooking the user's perception. This paper introduces a new spatio-temporal accessibility metric that combines four out of these six dimensions, including proximity, acceptability, adequacy, and availability. The metric considers uncertainty in measuring each dimension and addresses both user and provider perspectives in measuring the acceptability and adequacy dimensions. The variation in the metric across the disaster timeline serves as a proxy for community recovery. The metric aligns with common engineering-oriented functionality-based resilience frameworks as the functionality level of the providers has been incorporated in its development. Operating at the household level, the metric determines the ratio of post-disruption access time to the intended good or service against its pre-disruption access time and yields a unitless ratio between zero and one, with zero expressing a total loss in accessibility and one signifying the same level of accessibility as pre-disruption. The proposed metric, while being scientifically principled, is a practical tool whose output is easily understood even by non-expert individuals. The metric is illustrated for schools and pharmacies using the Lumberton Testbed and data collected following the 2016 flood in Lumberton, North Carolina after Hurricane Matthew. Findings provide new insight into recovery plan prioritization and can be used to trigger protective actions. The paper concludes by discussing issues and barriers related to developing and validating accessibility metrics while highlighting areas for future research.

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DISCS: An Approach for Accelerating the Development of Digital Twins for Smart Cities

McKee,

Dokter

2024

Digital Twin

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Virtual Testbeds for Community Resilience Analysis: State-of-the-Art Review, Consensus Study, and Recommendations

Cited by 11 publications

References 102 publications

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learning

Measuring Post-Disaster Accessibility to Essential Goods and Services: Proximity, Availability, Adequacy, and Acceptability Dimensions

DISCS: An Approach for Accelerating the Development of Digital Twins for Smart Cities

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