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Analysis of Post-Disaster Damage Detection using Aerial Footage from UWF Campus after Hurricane Sally
Book chapter   Peer reviewed

Analysis of Post-Disaster Damage Detection using Aerial Footage from UWF Campus after Hurricane Sally

Rafael de Sa Lowande, Andrew Clevenger, Arash Mahyari and Hakki Erhan Sevil
Frontiers in Education, e-Learning, e-Business, Image Processing, and Computer Vision: 21st International Conference, FECS 2025, 24th International Conference, EEE 2025, and 29th International Conference, IPCV 2025, Held as Part of the World Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2025, Las Vegas, NV, USA, July 21-24, 2025, Revised Selected Papers, pp.499-511
Communications in Computer and Information Science (CCIS), 2939, Springer Nature
05/12/2026
DOI: https://doi.org/10.1007/978-3-032-22202-2_38

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Abstract

In this study, we investigate the feasibility of detecting post-disaster damages through camera images obtained onboard an Unmanned Aerial Vehicle (UAV). Aerial footage from the University of West Florida (UWF) after being hit by hurricane Sally in 2020 is used in our study. Our goal is to automatically locate and identify all the roof damages caused by Sally on the university campus and compare two methods of detection. The first is a Convolutional Neural Network (CNN) based approach and the second is a cascade classifier model. We utilize a OpenCV based cascading classifiers and a TensorFlow Object Detection API model, and both are retrained on images hand annotated by our team to demonstrate the damage detection capabilities of these models. The aim of this study is to analyze feasibility and compare results between CNN and cascade classifier model for post-disaster damage detection to aid the effort of damage recovery after hurricanes.

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