# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 1 Introduction
2) Methods for canopy extraction: The canopy point cloud consists of a haphazard collection of leaves and branches extending from the tree trunk, and there are fewer regular features used for extraction. The current crown extraction relies on the location and geometric shape features of the trunk, and the crown points are attributed to the specified trunk by means of region growing algorithms and clustering. [PERSON] et al. [41] designed a mean drift clustering and shape analysis method to extract the crown points; [PERSON] et al. [42] used a bottom-up hierarchical clustering method; [PERSON] et al. [43] and [PERSON] et al. [44] attributed the canopy points to tree vertices. The region growth algorithm is the most commonly used method for extracting crown points through trunk points. The application of the algorithm to tree extraction involves innovations in two main areas: the selection of seed points and the choice of growth paths (growth constraints). Firstly, there is the selection of seed points based on local elevation differences and density [45], the selection of seed points based on the criterion that the trunk is near the horizontal center of the canopy and smaller in diameter than the canopy [46], and the use of the planar coordinates of the center of gravity of the super voxels as seed points for growth [34]. Secondly, for the selection of growth paths, researchers have designed competing region growth [29], breadth-first search algorithms [34] and dual growth methods [46], and to solve the problem of inter-canopy layer contact or overlap, region growth algorithms combined with [PERSON]'s method [47, 48] and Voronoi constraints [49, 50, 51] to achieve single tree segmentation. However, the shape and size of the tree, the distance between the tree and the LiDAR resulting in non-uniformity of the canopy density distribution and the absence of trunk points when the trunk is blocked by cars and green belts, which can easily lead to incorrect selection of seed point locations or incomplete growth during area growth, are still bottlenecks for improvement. In summary, the above methods of street tree extraction have the following shortcomings:  
* The extraction of trunks relies on more regular geometric features and is less accurate if parts of the trunk are bent or heavily obscured;* The results of canopy extraction depend to a large extent on the position of the trunk, which cannot be extracted when the trunk is heavily obscured;
* When there is a large gap between the canopy and trunk or between the canopy and crown, it is not possible to apply the regional growth algorithm to extract the canopy layer;
* The informatization of tree geometric parameters is the key to forestry surveys and 3D applications, and there is a lack of methods for the fine-grained analysis of tree parameters. To address the above-mentioned existing methods where the density of both trunk and crown points are unevenly distributed, this paper proposes a mobile LiDAR point cloud and region growth algorithm based on Gaussian distribution to extract the trunk and crown of the road environment and combines Voronoi (Tyson polygon) constrained segmentation to automate the segmentation of overlapping single trees to achieve 3D fine information and geometric parameter extraction of single trees. The remainder of this paper is organized as follows: Section 2 illustrates the methodology, Section 3 reports the experimental area and results, and Section 4 reports the discussion. Finally, Section 5 draws the conclusions of the work.