# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 4 Conclusions and Future Work  
To address the problem of an apparent artificial segmentation plane between adjacent tree crowns caused by hard segmentation, we, in this paper, propose a soft segmentation algorithm combining KNN with the contour shape constraints. Based on the segmented individual tree point set, the tree crown silhouette surface can be reconstructed with realistic figures, which is beneficial for shape modeling and crown analysis. Experimental results show that the segmentation performance has also been significantly improved in view  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Steps** & **Forest A** & **Forest B** & **Forest C** & **Forest D** & **RUSH06** \\ \hline Down-sampling & 0.0434 & 0.0376 & 0.0479 & 0.0851 & 0.7864 \\ Region Growing & 0 & 0 & 0 & 0 & 3.5919 \\ Layer Partitioning & 0.031 & 0.0378 & 0.0394 & 0.033 & 0.1098 \\ Roots Detect & 0.0036 & 0.0041 & 0.0049 & 0.0018 & 0.014 \\ Delaunay + Voronoi & 0.0024 & 0.0022 & 0.0029 & 0.0037 & 0.0176 \\ Init Segmentation & 0.0295 & 0.0279 & 0.0534 & 0.3537 & 0.6606 \\ Init Contour Build & 0.0111 & 0.0108 & 0.012 & 0.0214 & 0.0436 \\ Refine with KNN & 0.0777 & 0.0895 & 0.1076 & 0.2674 & 1.083 \\ Refine with Contour & 0.0119 & 0.0128 & 0.014 & 0.0298 & 0.0697 \\ Total & 0.2106 & 0.2227 & 0.2821 & 0.7959 & 6.3766 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The running time (unit: s) of segmentation experiments of five forest point clouds.  
Figure 19: Comparison of the time cost of each algorithm step when the down-sampling is (or is not) adopted as one step of preprocessing.  
of three quantitative indicators, Acc, mAcc, and mIoU; the time efficiency of point cloud segmentation has been improved by using down-sampling.  
Although the soft segmentation method we propose here provides efficient segmentation of canopy overlay areas and improves speed and accuracy, this method needs more actual data to validate it further. Therefore, one of the future research directions is using the soft segmentation method to test more forest point cloud data, where the overlapping patterns of the canopy are more complex. Hence, the soft segmentation approach here needs to be further expanded. Another promising research direction is destructive laser point cloud data acquisition of forests to obtain accurate canopy shapes that are not accurately measured when they overlap each other. In addition, it is also meaningful to study the relationship between the distance between trees and the shape of the overlapping area of the canopy using this laser scanning point cloud data.  
Conceptualization, M.D. and G.L.; methodology, M.D. and G.L.; software, M.D.; validation, M.D. and G.L.; formal analysis, M.D.; investigation, M.D.; resources, M.D. and G.L.; data curation, M.D.; writing--original draft preparation, M.D.; writing--review and editing, M.D. and G.L.; visualization, M.D.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Academy of Railway Sciences Corporation Limited, grant number 2021 YJ197.  
Not applicable.  
The first author can provide data including Forest A, B, C, and D; please send an email to get in contact; for RUSH06, please refer to TERN ([[https://portal.tern.org.au/metadata/23868](https://portal.tern.org.au/metadata/23868)]([https://portal.tern.org.au/metadata/23868](https://portal.tern.org.au/metadata/23868)), accessed on 1 March 2023).  
The authors declare no conflict of interest.