Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 955–961, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-955-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 955–961, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-955-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

A DEEP LEARNING FRAMEWORK FOR ROADS NETWORK DAMAGE ASSESSMENT USING POST-EARTHQUAKE LIDAR DATA

S. T. Seydi and H. Rastiveis S. T. Seydi and H. Rastiveis
  • Dept. of Photogrammetry and Remote Sensing, School of Surveying and Geomatics Eng., University of Tehran, Tehran, Iran

Keywords: LiDAR Point Cloud, Earthquake, Deep learning, Conventional Neural Network, Roads damage map

Abstract. Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo 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 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.