LAUSR

ABSTRACT Hurricanes cause significant property loss every year. A substantial part of that loss is due to trees destroyed by wind. The destroyed trees block roads and produce a large amount of debris that needs to be cleaned after the hurricane. This paper presents a modular framework to assess the consequence of tree failure due to high winds, focusing on the road closure probability and the debris estimation. Key modules of the proposed framework include Convolutional Neural Networks (CNNs) for tree recognition, tree failure assessment, and tree failure consequences. A significant difference between the proposed framework and other assessment models is that the proposed framework can estimate the road closure probability and the amount of debris before the hurricane, which may help authorities to prevent the potential loss by acting before it happens. CNNs for tree recognition module uses satellite imagery as an input and is composed of two networks. Network-I recognizes the tree locations and Network-II classifies the tree species. The tree failure assessment module uses the selected tree images to approximate the tree parameters and calculate the fragility functions for all the trees that can have an impact in the affected area. The last module estimates the consequence of tree failure such as the fragility of a road, a probability of road closure, and the amount of tree debris produced by the hurricane. As an illustrative example, a region in the City of Tallahassee, Florida has been investigated. The results derived by the proposed approach were validated based on the data obtained after Hurricane Hermine. The proposed approach can help city and state authorities to better plan for the adverse consequences of tree failures caused by hurricane winds.