Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine

Abstract

The commonly used artificial visual interpretation and existing object-oriented computer automatic recognition methods have some disadvantages, such as low efficiency and insufficient accuracy in recognizing earthquake-induced building damage in unmanned aerial vehicle (UAV)-based images. In this paper, we report the latest progress in research on machine learning algorithms in artificial intelligence, then propose a new method of recognizing earthquakeinduced building damage. Using the bag of words (BoW) model, scale-invariant feature transformation (SIFT) characteristics were clustered to build an eigenvector tag library with K clustering centers as visual words. After images were expressed by visual words as eigenvectors with unified dimensions, a histogram intersection kernel (HIK) was then employed to construct the histogram intersection kernel support vector machine (HIK-SVM) to classify images and recognize earthquake-induced building damage. Building damage due to the magnitude 6.0 earthquake that occurred in Luxian, Sichuan Province on September 16, 2021 was analyzed as an example. When the proposed method was applied to recognize earthquake damage using UAV-based images, the average recognition accuracy reached 91.7%. The experimental results verified the feasibility and validity of the proposed method.

Information Acquisition and Seismic Damage Prediction of Masonry Structures in Rural Areas Based on UAV Inclined Photogrammetry

“…Rapid decision-making before and after earthquake disasters is possible thanks to real-time data transmission, according to Mishra and Singh (2023). Technological advances have made seismic risk assessment more complete and eliminated the necessity for on-site inspections in dangerous or remote sites, reducing human assessor risk (Ying Zhang, Guo, Yin, Zhao, & Lu, 2023). Kumar et al (2023) report that machine learning and AI have greatly improved unmanned aerial vehicle seismic risk assessment accuracy.…”