Simulation of Street Tree Pruning for the Visibility of Traffic Signs to Drivers Using MLS Point Clouds.

Street trees providing ecological and cultural functions may block traffic signs from the driver's view if they are not properly maintained. Aiming at the problems of low automation and poor refinement of the detection of tree areas blocking traffic signboards and the inability to make pruning simulation and quantification of the occlude branches, in this letter, the detection of trees to be maintained and the simulation of branch pruning are studied based on mobile laser scanning (MLS) point clouds. First, the tree areas that block the cantilever traffic signs from the driver's view are automatically and accurately detected based on the line of sight analysis. Then, via structured modeling, the pruning effect of trees under different pruning degrees is simulated in 3-D, and four indicators (the number of pruning positions, the pruning branch volume, the pruning ratio of branch volume, and the pruning ratio of leaves) are proposed to quantify the pruning. The application in four different scenes proves the effectiveness of the proposed method. This research is helpful to automatically and accurately identify the street tree branches to be maintained and provides an important reference for the formulation of branch pruning schemes that can comprehensively balance the pruning cost, tree ecological benefits, and cultural benefits.