Object-Oriented Classification of Lidar Data for Post-Earthquake Damage Detection

Abstract

<p><strong>Abstract.</strong> The collapse of buildings during the earthquake is a major cause of human casualties. Furthermore, the threat of earthquakes will increase with growing urbanization and millions of people will be vulnerable to earthquakes. Therefore, building damage detection has gained increasing attention from the scientific community. The advent of Light Detection And Ranging (LiDAR) technique makes it possible to detect and assess building damage in the aftermath of earthquake disasters using this data. The purpose of this paper is to propose and implement an object-based approach for mapping destructed buildings after an earthquake using LiDAR data. For this purpose, first, multi-resolution segmentation of post-event LiDAR data is done after building extraction from pre-event building vector map. Then obtained image objects from post-event LiDAR data is located on the pre-event satellite image. After that, appropriate features, which make a better difference between damage and undamaged buildings, are calculated for all the image objects on both data. Finally, appropriate training samples are selected and imported into the object-based support vector machine (SVM) classification technique for detecting the building damage areas. The proposed method was tested on the data set after the 2010 earthquake of Port-au-Prince, Haiti. Quantitative evaluation of results shows the overall accuracy of 92 % by this method.</p>

Rapid Automatic Detection of Collapsed Buildings with LiDAR Data After an Earthquake

“…In current studies that use satellite imagery (Xu et al, 2019, Ma et al, 2019, Li and Tang, 2020, Convolutional Neural Networks (CNN) are heavily utilized. In a study by Rastiveis et al (2018), the overall accuracy rate reached 92% by using not only satellite imagery but also LiDAR and vector data. Another study proposed a supervised learning model for damage classification by combining CNN features and 3D point clouds (Vetrivel, 2018).…”

A Deep Learning Framework for Roads Network Damage Assessment Using Post-Earthquake Lidar Data

“…Pixel-level algorithms are fast and easy-to-implement which focus on extracting spatial and spectral features from the data in level of pixels. On the other hand, the object-level damage assessment methods are based on analysing a number of homogeneous segments (called image-objects) extracted from a segmentation technique (Rastiveis et al, 2018). They are usually time-consuming and complex due to the segmentation process.…”