Automated Road Extraction with Rapid Visibility During Eartquakes using LiDAR Data

The design of road routes, especially in urban areas, is crucial for effective transportation. Generally, image data and other geographic or satellite-based data sources are required for active extraction. However, in an emergency, such as an earthquake, this may not be adequate, therefore faster and more effective solutions may be required. This is achieved using automatic extraction using the Qgis plugin, Mapflow plugin and Rule Based Feature Extraction (RBFE) method using Light Detection and Ranging (LiDAR) point cloud data.The study found that the RBFE approach has an accuracy of 89%, while Mapflow has an accuracy of 92%. To improve the results, the Portion of Visible Sky (PCV) method and LiDAR-Digital Surface Model (DSM) image were used. At the end of the process, potential obstacles, such as trees or cars on the road, can be identified, which humans may not be able to detect remotely. In the future, the proposed methods could be further developed with the help of the OpenStreetMap data set.