# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 5 Conclusions  
In this study, lightweight LiDAR was utilized to collect data from typical urban road intersections. After preprocessing steps such as registration and stitching, a point cloud semantic segmentation model based on the improved RandLA-Net was applied based on the actual characteristics of road intersection scene point clouds. The main contributions of this study are as follows:  
(1) Improved feature aggregation module: An improved feature aggregation module was built in addition to the original network. This module introduced a feature similarity matrix to the spatial information encoding layer, selecting neighborhood point features similar to the central sampling point's local features as the neighborhood feature information. In the aggregation pooling layer, the results of max pooling, average pooling, and attention pooling were aggregated to extract both local neighborhood features and global information from the point cloud. To ensure a relatively balanced contribution of different category samples to the model's loss function during training, a weighted cross-entropy loss function was also established. This loss function reduced the negative impact of uneven numbers of point clouds across categories in road scenes. (2) Semi-automated processing framework: This study presented a semi-automated framework that integrates the use of lightweight LiDAR for data collection, point cloud registration, and semantic segmentation. This framework can extract the features of 3D point cloud data in road intersection scenarios, providing a reference for extracting operational road infrastructure information and digital asset management. Compared to RandLA-Net, this method improved the overall accuracy by 2.40% and 2.85% and the average F1 scores by 1.67% and 1.52%, respectively, for the two road intersection point \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network Model** & **Metrics (\%)** & **Road Surface** & \
\begin{tabular}{c} **High** \\ **Vegetation** \\ \end{tabular} &
\begin{tabular}{c} **Low** \\ **Vegetation** \\ \end{tabular} & **Sidewalk** & **Guardrail** & **Pole** \\ \hline \multirow{3}{*}{RandLA-Net} & F1 Score & 92.39 & 87.95 & 65.67 & 64.44 & 80.95 & 83.12 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 86.76 & & & \\  & Avg. F1 & & & 79.09 & & & \\ \hline \multirow{3}{*}{Ours-A} & F1 Score & 93.35 & 88.12 & 67.94 & 64.74 & 80.95 & 83.26 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 87.65 & & & \\  & Avg. F1 & & & 79.73 & & & \\ \hline \multirow{3}{*}{Ours-B} & F1 Score & 95.48 & 88.14 & 68.62 & 64.97 & 80.97 & 83.32 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 89.48 & & & \\  & Avg. F1 & & & 80.25 & & & \\ \hline \multirow{3}{*}{Ours} & F1 Score & 95.63 & 88.53 & 69.97 & 65.01 & 81.00 & 83.51 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 89.61 & & & \\ \cline{1-1} \cline{2-8}  & Avg. F1 & & & 80.61 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the ablation experiments (Test Set 2). cloud test sets. The segmentation accuracy was improved in each category, with the road surface area prediction accuracy reaching 94.48% and 94.79%, respectively. However, there are some limitations in this study. (1) Limited field data collection: Due to the limited field data collection, we trained and tested the point cloud dataset of experimental intersections only on certain effects. In the future, the improved RandLA-Net will be used to train other road intersection point clouds, further enhancing its generalization performance through model parameter adjustments and network structure optimization. (2) Misidentification of road vegetation, sidewalks, and other facilities: The proposed semantic segmentation method misidentified road vegetation, sidewalks, and other facilities. Subsequent considerations include merging road intersection point clouds with aerial images, applying oblique photography, and using other surveying and mapping data. Introducing multi-source data features may better capture the structural information of objects in the above categories.