Automated Satellite-Based Assessment of Hurricane Impacts on Roadways
Michele Gazzea, Alican Karaer, Mahyar Ghorbanzadeh, Nozhan Balafkan, Tarek Abichou, Eren Erman Özgüven, Reza Arghandeh
Abstract
During extreme weather events, such as 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 article proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a covoting 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.