Abstract
Abstract. The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this research is to develop an efficient algorithm for road network extraction from airborne LiDAR data using supervised classification approach. The proposed algorithm first classifies the input data into the road and non-road features using modified maximum likelihood classification approach. Then Digital Terrain Model (DTM) mask is generated by removing non-ground features from Digital Surface Model using hierarchical morphology and road candidate image if obtained. The parking lots are removed and road network is extracted successfully.
Highlights
The road network often gets modified due to various developmental activities and an upgraded map of road is helpful for the various applications
The various approaches have been developed for the automatic road network extraction but still their accuracy, completeness and automation are a challenge among the researchers
The second module consists of generation of digital terrain model (DTM) mask and its integration with classified image obtained from modified maximum likelihood (ML) classification in the first module
Summary
The road network often gets modified due to various developmental activities and an upgraded map of road is helpful for the various applications. An approach is presented in this paper that uses intensity and depth information for rapid extraction of road features from LiDAR point cloud. The method of Zhao et al (2012) removed the non-ground points from image and applied EM classification to generate the road candidate image. They performed direction based cumulative voting to remove parking lots and used geometrically guided gap filling. The road network extraction methodology proposed in this research paper uses intensity and depth images generated from LiDAR data. Thereafter, the parking lots are removal followed by extraction of road centrelines
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