Automated Satellite-Based Assessment of Hurricane Impacts on Roadways

Abstract

During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using highresolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018.

When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

“…Bauer et al 2021, Dueben et al 2022, Bi et al 2023, Ebert-Uphoff and Hilburn 2023, and an opportunity for novel combinations of data (including those derived from new observational tools such as lowcost sensors and social media, as well as Internet of Things) that can lead to tailored forecasting products (WMO 2023). In the field of DRR, AI is contributing to (near-)real-time detection and forecasting of floods (Tiggeloven et al 2021); (near-)real-time detection of wildfires (Sousa et al 2020a); (near-)realtime detection and forecasting of volcano-seismic events (Cortés et al 2021); and ex-post impact assessment (Gazzea et al 2022), among other applications.…”

Damaged buildings classification using residual network

“…Satellite images have been widely used to evaluate disaster situation across the world. Balaji Ramesh et al have used satellite images to detect flooding areas post hurricanes [2], while Michele Gazzea et al has developed some deep learning models for the assessment of the damage of railways [3]. Effective methods of evaluating and assessing the damage of damaged facilities, including buildings, are of high value when handling with the aftermath: both saving people in time, and diminishing the economic loss as much as possible.…”

(Retracted) Hurricane damage assessment in satellite images using hybrid VGG16 model

“…Manual methods for damage detection are prone to error as well being as time consuming. Hence, machine learning (ML) 4 and deep learning (DL) come into the picture.…”