Analysis of post-disaster damage detection using aerial footage from UWF campus after hurricane Sally

Abstract/Description: 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 of classifiers model. We utilize cascading classifiers from the OpenCV Python library and a TensorFlow Object Detection API model both 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.
Subject(s): Cascade classifier model
Post-disaster damages
Hurricanes
Unmanned Aerial Vehicle (UAV)
Convolutional Neural Network (CNN)
Date Issued: 2021