## 5 Conclusions  
On the basis of the comprehensive analysis of the experimental results in this paper, it can be seen that, on a whole, the method in this paper had a relatively good classification effect on three types of ground objects, namely, trees, impervious surfaces, and roofs, but a relatively poor classification effect on fences, shrubs, and cars.  
The experimental comparison with the data from Shashi city showed that the classification accuracy of different ground objects was related to the choice of point-cloud  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Power Line** & **Car** & **Low\_Veg** & **Imp\_Surfaces** & **Roof** & **Hedge** & **Facade** & **Shrub** & **Tree** \\ \hline PointNet [13] & 0.8 & 23.2 & 32.1 & 47.6 & 84.7 & 2.3 & 5.7 & 15.4 & 76.2 \\ \hline Ours & 0.5 & 58.7 & 65.4 & 78.4 & 95.8 & 15.8 & 16.9 & 27.5 & 92.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Vaihingen City datasets for each class of IoU (%).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**F\({}_{\mathbf{1}}\)**} & **OA** \\ \cline{2-7}  & **Low\_Veg** & **Imp\_Surface** & **Car** & **Roof** & **Shrub** & **Tree** & **OA** \\ \hline Ours & 88.1 & 94.8 & 55.8 & 97.5 & 51.1 & 93.6 & 89.1 \\ \hline WhuY4 & 82.7 & 91.4 & 74.7 & 94.3 & 47.9 & 82.8 & 84.9 \\ \hline UM & 79.0 & 89.1 & 47.7 & 92.0 & 40.9 & 77.9 & 80.8 \\ \hline NANJ2 & 88.8 & 91.2 & 66.7 & 93.6 & 55.9 & 82.6 & 85.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy comparison of different classification methods (%).  
Figure 9: Schematic diagram of point-cloud classification error in specific region. (**(a)**: point cloud displayed with elevation; **(b)** indicates that there is a ditch where the box is framed, and the height difference between the two sides of the ditch is large, so the classification effect is not good.)  
density and the sphere radius. By comparing our proposed method with the existing cloud classification methods, it can be seen that the proposed method has a great advantage in classification accuracy and can provide reliable information in the application of point clouds, such as in three-dimensional urban modeling. This study used a point-cloud method based on semantic segmentation in one direction using low-level characteristics and selected point-cloud scales to maintain a satisfactory classification accuracy and simplify the time complexity of the algorithm. The PointNet neural network structure and the feature selection were optimized, improving the accuracy and efficiency of the algorithm.  
Conceptualization, P.H.; methodology, P.H. and M.F.; software, W.L.; validation, Z.M. and M.W.; resources, G.G.; data curation, Z.M.; writing--original draft, M.F.; writing--review and editing, M.W.; supervision, G.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China [grant No. 41901285 and No. 41901296] and the Funds for Henan Province young talent support project [grant No. 2021 HYTP009].  
Not applicable.  
The data that support the findings of this study are available from the corresponding author ([PERSON]), upon reasonable request.  
The authors declare no conflict of interest.