# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 4 Discussion  
### Comparative Analysis  
In order to objectively evaluate the performance of our method in segmenting single street trees within the data of the 7 th National LiDAR Conference Data Processing Competition 2021, the latest validated method of [PERSON] et al. [32] was chosen for comparative analysis, who used the same experimental area as in this paper. [PERSON] et al. proposed a street tree extraction and segmentation method based on the spatial geometric features of object primitives, which applying a normal vector angle threshold between neighborhood points to divide the original point cloud into different primitives, extracting trunk points through the linear features between primitives and the aspect ratio of the primitive bounding box, and proposing a voxel shortest path analysis method to obtain an individual tree optimization separation result. [PERSON] et al. method uses five metrics, namely extraction rate, matching rate, commission error, omission error and F1 score, to evaluate the accuracy of individual tree segmentation results, respectively, which are slightly different from the accuracy evaluation metrics in this paper. Where the matching rate is the same as the recall \(Rec\) in this paper, the F1-score is the same as the F1-score, and the accuracy \(Pre\) is equal to 1 minus the commission error. The results of [PERSON] et al. 's method accuracy evaluation of conversion and a comparison with our method are shown in Table 5.  
As can be seen from Table 4, the difference in recall between the two methods for extracting single trees is not significant, while the accuracy of our method is much greater than that of [PERSON] et al. It is shown that both methods provide a more complete extraction of street trees in the experimental area, but the advantage of our method is that there is less identification of other non-tree points as single trees. Due to the primitive connectivity proposed by [PERSON] et al. 's method, some of the non-tree points connected to tree points are extracted, which in turn affects the accuracy of the extraction. Moreover, there are \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Area** & **TP** & **FP** & **FN** & **Pre** & **Rec** & **F1-Score** \\ \hline \(S_{1}\) & 82 & 3 & 10 & 96.47\% & 89.13\% & 92.65\% \\ \(S_{2}\) & 70 & 3 & 2 & 95.89\% & 97.22\% & 96.55\% \\ \(S_{3}\) & 39 & 3 & 11 & 92.85\% & 78.00\% & 84.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy evaluation of the experimental results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Method** & **_Pre_** & **_Rec_** & **F1-Score_** \\ \hline \(S_{1}\) & Hui [32] & 90.00\% & 88.04\% & 89.01\% \\ \(S_{1}\) & Ours & 96.47\% & 89.13\% & 92.65\% \\ \(S_{2}\) & Hui [32] & 75.27\% & 97.22\% & 84.85\% \\ \(S_{2}\) & Ours & 95.89\% & 97.22\% & 96.55\% \\ \(S_{3}\) & Hui [32] & 61.29\% & 76.00\% & 67.86\% \\ Ours & 92.85\% & 78.00\% & 84.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of precision evaluation results. many weeping willows in the experimental area \(S_{3}\), and these willow branches close to the ground are often mistaken for branches, thus increasing the number of incorrectly extracted row trees [32] and leading to a lower extraction accuracy by the method of [PERSON] et al. In contrast, we propose a pseudo-trunk identification method that improves the accuracy of willow extraction in \(S_{3}\) through the geometric features and groundedness of candidate trunk clusters that distinguish them from pendants. Furthermore, in terms of completeness of irregular trunk (stem) point extraction, [PERSON] et al. 's method of extracting trunk points through linear features between primitives and the aspect ratio of the primitive bounding box is suitable for inclined trunk extraction. However, there were willows with partially bent trunks in the experimental area \(S_{3}\), and the method of [PERSON] et al.