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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.