Light Detection and Ranging (LiDAR) data has been used to model earth surface in an easy and economic way. As technology is developed the application of LiDAR data is also widely expanded to various areas, such as hydrological modeling, telecommunication service and urban planning. Finding accurate road networks is one of the common applications from massive LiDAR data. A novel algorithm to extract road points has been developed based on both the intensity and height information of data points. First the robustness of the sequential algorithm has been verified with real data points. Then a parallel algorithm has been developed by applying smart area partitioning. The performance of a parallel algorithm showed us a close linear speedup with the use of up to four processors. Experimental results from the parallel algorithm are presented in this paper in detail and demonstrate the robustness of the proposed method.
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