Automated extraction of roads from mobile laser scanning point clouds by image semantic segmentation
Bo YU1, Junjun ZHANG2, Chungeng LI1(), Jubai AN1
1. School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China 2. Beijing Dilu Technology Co., Ltd., Beijing 100193, China
Mobile laser scanning can acquire lots of dense point clouds. Therefore, how to get high-quality road point clouds is a problem worthy of further study. This paper proposes a method for automatic extraction of roads from mobile laser scanning point clouds by image semantic segmentation. The authors use a four-step strategy: First, semantic segmentation images are created using 2D panoramic images. Then, fusion and matching are conducted to get rough classification results. After that, the 3D Hough transform is used to get the segmentation plane before fitting. Finally, a finely classified point cloud is obtained through local optimization operations. The authors extracted and evaluated two different points of cloud data on urban roads. The accuracy and integrity are all over 99%. The extraction quality is high enough to adapt the application requirements in practice. The method proposed by the authors can extract road point clouds in different situations and has less primitive constraints on point cloud data. It shows a significant improvement in both universality and robustness compared with other methods.
于博, 张军军, 李春庚, 安居白. 图像语义分割辅助的车载激光点云道路提取方法[J]. 国土资源遥感, 2020, 32(1): 66-74.
Bo YU, Junjun ZHANG, Chungeng LI, Jubai AN. Automated extraction of roads from mobile laser scanning point clouds by image semantic segmentation. Remote Sensing for Land & Resources, 2020, 32(1): 66-74.
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