## 3 Experimental Data
### Classification Results for Point Clouds  
The intersection over union (IoU) [32] is obtained by dividing the overlap between two regions by the separate parts of these regions. The result is compared with the calculated result of this IoU through the set threshold and is expressed as a percentage. It can be simply solved as the union set on the intersection ratio of the detection result and ground truth, i.e., the intersection ratio of the detection result and \(IoU\) (see Equation (1)). \[IoU=\frac{Detection\ Result\cap GroundTruth}{Detection\ Result\cup GroundTruth}. \tag{1}\]  
Overall accuracy (OA) was used to evaluate the overall classification accuracy of all categories, which is defined as the percentage of the total number of correctly classified points over the total number of points. Figure 4: Point-cloud data displayed by elevation in Shashi area and orthophoto image. We used the \(F_{1}\) score as a comprehensive evaluation index. The formula for calculating the \(F_{1}\) fraction is as follows:  
\[F_{1}=2\cdot\frac{P_{\text{precision}}\cdot P_{recall}}{P_{\text{precision}}+P_{ recall}}. \tag{2}\]  
In order to verify the multiscale and multi-feature algorithm proposed in this paper, the same data and the same network were first used for single-scale and single-feature (SS) point-cloud data (i.e., data containing only XYZ information); then, verification using single-scale, multi-feature (SM) data (i.e., containing XYZRGB and R = 1 m features, a total of 11 features) and multiscale, multi-feature (MM) data (all scales and all features) was carried out. A comparison of the OA results is shown in Table 4.  
As can be seen from Figure 5, the proposed multiscale PointNet neighborhood algorithm (MM) had the highest overall classification accuracy and best classification effect. The overall accuracy was 88.1%. From the above quantitative evaluation results, fusion based on spectral information and covariance characteristics (SM) after all the features of the point cloud was shown to promote classification accuracy. The overall accuracy of SM was 19.2 percentage points higher than that of SS. After the fusion of spectral information and the covariance characteristics (MM) of the two scales, the accuracy was further improved by 8.4 percentage points compared with the single-scale SM. Among the classification categories, the accuracies of impervious ground, tree, and roof were improved by 19.7 percentage points, 30 percentage points, and 31.3 percentage points, respectively. It can be seen that the attribute information of multiscale and multi-neighborhood point clouds can be effectively enhanced, thus achieving a more accurate classification of various ground objects. Figure 6 shows the comparison of specific classification results. The actual value, SS, SM, and MM classification results are shown in sequence. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Low\_veg (\%)** & **Car (\%)** & **Imp\_Surface (\%)** & **Shrub (\%)** & **Tree (\%)** & **Roof (\%)** & **OA (\%)** \\ \hline SS & 27.4 & 15.5 & 70.4 & 30.8 & 55.4 & 60.1 & 60.5 \\ \hline SM & 39.5 & 37.7 & 87.7 & 49.4 & 66.7 & 89.3 & 79.7 \\ \hline MM & 45.5 & 32.4 & 90.1 & 54.7 & 85.4 & 91.4 & 88.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results and overall accuracy. Figure 5: All kinds of ground features and overall classification accuracy. According to the above scale comparison, the classification accuracy of the proposed method was the highest, and the classification effect was the best. When this method was applied to the data in Shashi district, the results were as shown in Figure 7. Here, the classification accuracy reached 92.4%. TerraSolid software cannot classify road point clouds. In order to make the data comparative, this paper divided the ground points classified by TerraSolid into bare land to evaluate the classification results. As can be seen from Figure 7, TerraSolid had better classification results for tall trees and artificial buildings.