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Browsing by Author "Whitehurst, Daniel"

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    Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development
    Whitehurst, Daniel; Friedman, Brianna; Kochersberger, Kevin B.; Sridhar, Venkataramana; Weeks, James (MDPI, 2021-04-30)
    Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments.
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    Post-Flood Analysis for Damage and Restoration Assessment Using Drone Imagery
    Whitehurst, Daniel; Joshi, Kunal; Kochersberger, Kevin; Weeks, James (MDPI, 2022-10-04)
    With natural disasters continuing to become more prevalent in recent years, the need for effective disaster management efforts becomes even more critical. Specifically, flooding is an extremely common natural disaster which can cause significant damage to homes and other property. In this article, we look at an area in Hurley, Virginia which suffered a significant flood event in August 2021. A drone is used to capture aerial imagery of the area and reconstructed to produce 3-dimensional models, Digital Elevation Models, and stitched orthophotos for flood modeling and damage assessment. Pre-flood Digital Elevation Models and available weather data are used to perform simulations of the flood event using HEC-RAS software. These were validated with measured water height values and found to be very accurate. After this validation, simulations are performed using the Digital Elevation Models collected after the flood and we found that a similar rainfall event on the new terrain would cause even worse flooding, with water depths between 29% and 105% higher. These simulations could be used to guide recovery efforts as well as aid response efforts for any future events. Finally, we look at performing semantic segmentation on the collected aerial imagery to assess damage to property from the flood event. While our segmentation of debris needs more work, it has potential to help determine the extent of damage and aid disaster response. Based on our investigation, the combination of techniques presented in this article has significant potential to aid in preparation, response, and recovery efforts for natural disasters.