Roadway Vulnerability Assessment against Hurricanes Using Satellite Images

Abstract

Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially those caused by extreme events, quickly affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult because of the stochastic nature of such events. Nevertheless, it is possible to know in advance which areas are more vulnerable and plan accordingly. This paper aims to use remote sensing techniques based on satellite images to detect roadways’ vulnerabilities to hurricanes. Each road is assigned a vulnerability score based on the information retrieved from the satellite image and other geographic information system (GIS) data, which can be used to create a vulnerability map of the area. Both vegetation exposure along the roads and the consequences of the closures are considered in the vulnerability assessment. The study area has been selected in Tallahassee, Florida, U.S., where a high-resolution satellite image was acquired in September 2018, 2 weeks before Hurricane Michael. The findings of this work can help management teams and city responders to identify the most vulnerable regions at risk of disruption and organize resources before the event.

Traffic monitoring system design considering multi-hazard disaster risks

“…In this study, we are considering the road network disruption after a hurricane. From the literature 19 and our experience, we know that trees (e.g., fallen trees, tree debris, etc.) and floods are some of the leading causes of roadway closure and damages following extreme weather events, like hurricanes.…”

“…A minority of them experienced a positive trend (growth) and others a negative trend (decay). To isolate the most significant negative trend in , similarly to 19 , we empirically set a threshold Th as , where and are the mean and standard deviation of the distribution. This is justified by the fact that we are interested in detecting changes in the vegetation index that are statistically significant in relation to the average changes in the whole area.…”

Traffic Monitoring System Design considering Multi-Hazard Disaster Risks

“…A minority of them experienced a positive GCVI trend (growth) and others a negative GCVI trend (decay). To isolate the most significant negative trend in GCVI , similarly to 19 , we empirically set a threshold Th as Th = µ − σ , where µ and σ are the mean and standard deviation of the GCVI distribution. This is justified by the fact that we are interested in detecting changes in the vegetation index that are statistically significant in relation to the average changes in the whole area.…”