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1. Post-Hurricane Vegetative Debris Assessment Using Spectral Indices Derived from Satellite Imagery

   https://doi.org/10.1177/03611981211029921  

   Karaer, Alican; Ulak, Mehmet Baran; Abichou, Tarek; Arghandeh, Reza; Ozguven, Eren Erman (December 2021, Transportation Research Record: Journal of the Transportation Research Board)

   Transportation systems are vulnerable to hurricanes and yet their recovery plays a critical role in returning a community to its pre-hurricane state. Vegetative debris is among the most significant causes of disruptions on transportation infrastructure. Therefore, identifying the driving factors of hurricane-caused debris generation can help clear roadways faster and improve the recovery time of infrastructure systems. Previous studies on hurricane debris assessment are generally based on field data collection, which is expensive, time consuming, and dangerous. With the availability and convenience of remote sensing powered by the simple yet accurate estimations on the vigor of vegetation or density of manufactured features, spectral indices can change the way that emergency planners prepare for and perform vegetative debris removal operations. Thus, this study proposes a data fusion framework combining multispectral satellite imagery and various vector data to evaluate post-hurricane vegetative debris with an exploratory analysis in small geographical units. Actual debris removal data were obtained from the City of Tallahassee, Florida after Hurricane Michael (2018) and aggregated into U.S. Census Block Groups along with four groups of datasets representing vegetation, storm surge, land use, and socioeconomics. Findings suggest that vegetation and other land characteristics are more determinant factors on debris generation, and Modified Soil-Adjusted Vegetation Index (MSAVI2) outperforms other vegetation indices for hurricane debris assessment. The proposed framework can help better identify equipment stack locations and temporary debris collection centers while providing resilience enhancements with a focus on the transportation infrastructure. 

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2. Developing a Concept of Operations Template to Guide Collaborative Disaster Research Response Between Academic Public Health and Public Health Agencies

   https://doi.org/10.1017/dmp.2021.280  

   Khan, Amber S; Wittenauer, Rachel; Patel, Resham; Baseman, Janet; Miller, Aubrey; Errett, Nicole A (January 2023, Disaster Medicine and Public Health Preparedness)

   Abstract Research conducted in the context of a disaster or public health emergency is essential to improve knowledge about its short- and long-term health consequences, as well as the implementation and effectiveness of response and recovery strategies. Integrated approaches to conducting Disaster Research Response (DR2) can answer scientific questions, while also providing attendant value for operational response and recovery. Here, we propose a Concept of Operations (CONOPS) template to guide the collaborative development and implementation of DR2 among academic public health and public health agencies, informed by previous literature, semi-structured interviews with disaster researchers from academic public health across the United States, and discussion groups with public health practitioners. The proposed CONOPS outlines actionable strategies to address DR2 issues before, during, and after disasters for public health scholars and practitioners who seek to operationalize or enhance their DR2 programs. Additional financial and human resources will be necessary to promote widespread implementation of collaborative DR2 programs. 

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3. Meta-network Framework for Analyzing Disaster Management System-of-Systems

   Fan, Chao; Zhang, Cheng; Mostafavi, Ali (January 2018, IEEE System of Systems Engineering Conference (SoSE))

   The objective of this paper is to establish a meta-network framework to identify constituents in Disaster Management System-of-Systems (DM-SoS), conceptualize relationships and interactions among the constituents, and formulate quantitative measurements of DM-SoS performance for achieving network-centric operation and coordination in the context of disasters. With increasingly serious impacts of disasters on interdependent and heterogeneous systems, the improvement of effective and integrative disaster response and coordination is needed. However, some existing literature only proposed some frameworks for modeling disaster management systems, while another stream of studies only examined the social network analysis (SNA) for understanding the interactions between stakeholders. Thus, quantitative and integrative measurements in DM-SoS are missing. To address this knowledge gap, the authors created and discussed a metanetwork framework integrating various types of entities and relationships for quantitatively analyzing the performance of DM-SoS. First, this framework defined nodes and links in meta-metrics for abstracting constituents in disaster management. Second, some performance indicators (e.g., effectiveness, the extent of information sharing, and the extent of self-organization) were created to show the capacities of disaster systems, and the potential perturbations in disaster environment were translated by network theory. Finally, we examined the impacts of perturbations on the indicators and assessed the performance by integrating overall indicators. This study highlighted the significance of quantitative measurements and an integrative perspective on analyzing efficiency and effectiveness of disaster response and coordination. The study also provides implications for making comparisons of different response strategies for decision makers to achieve resilient disaster management systems. 

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4. Imaging-to-Simulation Framework for Improving Disaster Preparedness of Construction Projects and Neighboring Communities

   https://doi.org/10.1061/9780784480830.029  

   Ham, Youngjib; Lee, Seung Jae; Chowdhury, Arindam Gan (June 2017, ASCE International Workshop on Computing in Civil Engineering 2017)

   Unstructured construction sites including incomplete structures and unsecured resources (e.g., materials, equipment, and temporary facilities) are among the most vulnerable environments to windstorms such as hurricanes. Wind-induced cascading damages cause substantial losses, disruption, and considerable schedule delays in construction projects. Moreover, this would negatively affect neighboring buildings and interdependent infrastructures (e.g., electric power transmission or transportation systems), which triggers serious economic losses in our community. Nonetheless, prior works on disaster management mainly focused on post-disaster assessment and reconstruction process of built environments, and thus predicting potential risks associated with expected disasters for proactive preparedness remain largely unknown. This paper presents a new Imaging-to-Simulation framework that can uncover potential risks of wind-induced cascading damages to construction projects and their negative impacts on neighboring communities. The outcomes are expected to benefit our society as it will enhance current windstorm preparedness and mitigation plans, which ultimately promote public safety, property loss reduction, insurance cost reduction, and raise awareness of ‘Culture of Preparedness’ for disasters. 

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5. AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

   https://doi.org/10.3390/s20185223  

   Chen, Junjie; Li, Shuai; Liu, Donghai; Li, Xueping (September 2020, Sensors)

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   Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots. 

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