Automated Satellite-based Assessment of Hurricane Impacts on Roadways
Peer reviewed, Journal article
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Original versionGazzea, M., Karaer, A., Ghorbanzadeh, M., Balafkan, N., Abichou, T., Ozguven, E. E., & Arghandeh, R. (2021). Automated satellite-based assessment of hurricane impacts on roadways. IEEE Transactions on Industrial Informatics 10.1109/TII.2021.3082906
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 high-resolution 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
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