Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo turbo air m3f72-3-n a problem.This paper aims to provide and implement a deep learning-based method to determine the status of the streets network after an earthquake using LiDAR point cloud.The proposed framework composes of three main phases: (1) Deep features of LiDAR data are extracted using a Convolutional Neural Network (CNN).(2) The extracted features are used in a multilayer perceptron (MLP) neural network in which debris areas inside the road network are detected.(3) The amount of debris in each road is applied bovi-shield gold fp 5 l5 to damage index for classifying the road segments into blocked or un-blocked.
To evaluate the efficiency of the proposed framework, LiDAR point cloud of the Port-au-Prince, Haiti after the 2010 Haiti earthquake was used.The overall accuracy of more than 97% proved the high performance of this framework for debris detection.Moreover, analyzing damage assessment of 37 road segments based on the detected debris and comparing to a visually generated damaged map, 31 of the road segments were correctly labelled as either blocked or un-blocked.