# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 5 Conclusions  
This paper proposes a technique for automatic refinement extraction of roadside trees in vehicle-mounted LiDAR point clouds based on a Gaussian distribution of the region growth (GDRG) algorithm. A statistical method is designed and studied for single roadside tree segmentation and parameter information, including the GDRG algorithm based on trunk points and optimized seed points, single tree segmentation with crown point Voronoi diagram constraints and tree geometric parameter calculation, which can provide support for road environmental safety information, road safety information management and forest biomass estimation. This method solves, to a certain extent, the problems of classification extraction of curved or irregular tree trunks, low completeness of results due to small density of crown points and serious missing tree trunk points. At  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**X (m)**} & \multirow{2}{*}{**Y (m)**} & **Tree Height** & **Canopy Crown** & **Breast** & **Trunk** \\  & & & & **(m)** & **Width (m)** & **Dimension (m)** & **Height (m)** \\ \hline \multirow{3}{*}{\(S_{1}\)} & 1 & 431,863.346 & 3,895,619.663 & 6.372 & 4.644 & 0.499 & 2.803 \\  & 10 & 431,869.902 & 3,895,570.139 & 6.106 & 3.657 & 0.281 & 3.014 \\  & 20 & 431,883.642 & 3,895,471.374 & 6.579 & 4.634 & 0.324 & 2.504 \\ \hline \multirow{3}{*}{\(S_{2}\)} & 1 & 431,954.597 & 3,895,631.619 & 5.806 & 4.344 & 0.534 & 2.382 \\  & 10 & 431,957.175 & 3,895,612.356 & 5.661 & 4.022 & 0.626 & 2.175 \\  & 20 & 431,970.350 & 3,895,519.491 & 6.693 & 6.080 & 0.706 & 2.782 \\ \hline \multirow{3}{*}{\(S_{3}\)} & 1 & 431,925.301 & 3,895,408.774 & 13.851 & 9.336 & 1.027 & 2.555 \\  & 10 & 431,998.686 & 3,895,434.145 & 12.419 & 11.764 & 0.768 & 2.444 \\ \cline{1-1}  & 20 & 431,942.074 & 3,895,419.157 & 10.754 & 11.724 & 1.246 & 2.561 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical table of geometric parameters of trees.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Study Area** & **Standard Deviation of Position (m)** \\ \hline \(S_{1}\) & 0.0337 \\ \(S_{2}\) & 0.1481 \\ \(S_{3}\) & 2.378 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Standard deviation of center position.  
the same time, our method has more parameter thresholds and lower accuracy in the segmentation and extraction of willow tree groups due to the connection of buildings and trees; these issues are subject to further study in the future. In terms of application, the accurate extraction of the geometric parameters of the street tree point cloud is only part of the equation. The problem of missing target points due to points being obscured during the scanning process also requires tracking and completion of the missing points. The extracted points are used for 3D reconstruction to meet the needs of high-precision map construction.  
Conceptualization, Y.W.; methodology, Y.W., Y.L. and H.C.; software, Y.W. and Y.L.; validation, Y.L. and H.C.; formal analysis, Y.L. and H.C.; investigation, Y.L. and H.C.; resources, Y.W. and Y.L.; data curation, H.C. and S.L.; writing--original draft preparation, Y.L., H.C. and S.L.; writing--review and editing, Y.W. and Y.L.; visualization, Y.L. and H.C.; supervision, Y.W.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 41971423 and 31972951), the Natural Science Foundation of Hunan Province (No. 2020 JJ3020) and Open Fund of State Key Laboratory of Public Big Data of Guizhou University (No. PBD2022-02).  
Not applicable.