## 1 Introduction  
Urban surface coverage is important basic data for studying and managing urban environments [1]. Since the ground surface in urban areas is often covered by different features, it is difficult to accurately obtain the spatial and attribute information of each type of feature [2, 3]. Therefore, the use of 3D point-cloud data for quickly and accurately interpreting complex urban scenes has become a research hotspot. In a point-cloud scene, semantic segmentation is a crucial visual task enabling scene understanding. [PERSON] et al. [4] propose the semantic segmentation network RandLA-Net++, which could achieve accurate and fine semantic segmentation. [PERSON] et al. [5] proposed the bidirectional segmentation network (BiSeNetV2), which demonstrated high accuracy and high efficiency in real-time semantic segmentation. Meanwhile, there are also new research results in the processing of 3D point cloud data, which can be broadly classified into the following three categories: (1) multi-view-based methods, (2) voxel-based methods, and (3) primitive point-cloud-based methods. 1. Multi-view-based methods. The main idea of this method is to project 3D point clouds into 2D images from multiple views, perform semantic segmentation processing using 2D convolutional neural networks, and finally restore the images to the original 3D point cloud for representation. In 3D radar point-cloud processing, [PERSON] et al. [6] designed the spatial adaptive convolution (SAC) method and constructed SqueezeSegV3 for LiDAR point-cloud segmentation accordingly, which solved the common problem of different image locations affecting the feature distribution of the image, which in turn affected the network's performance [7]. Moreover, 3D-MiniNet was designed on the basis of MiniNet to achieve higher efficiency [8; 9]. [PERSON] et al. designed RangeNet++ with modifications and optimization on DarkNet to enable semantic segmentation; however, these methods have limited application and inevitable information loss during projection [10]. 2. Voxel-based methods. This method converts point clouds into voxels and then convolves them in 3D. This method can efficiently handle large-scale 3D data [11; 12]. However, the memory requirements of this method are too high, and there is too much data redundancy in the voxel representation. 3. On the basis of the original point@cloud approach, in the study of irregular point clouds, [PERSON] et al. [13] designed a new deep neural network, PointNet, which can be used to learn point cloud features point by point. PointNet does not convert the point cloud into any other data representation, but directly uses it as an input network. It retains the spatial features of point clouds to the maximum extent and has strong testing performance. The subsequent deep learning networks based on original point clouds can be broadly classified into several categories, such as multilayer perceptrons, graph convolutions, and RNNs. [PERSON] et al. [14] proposed a neighborhood search method that selects the appropriate search method according to the characteristics of each point, thus avoiding the shortcomings of manual selecting search methods. [PERSON] et al. and [PERSON] et al. [15; 16] used different convolution weights to process point clouds, an approach which also solved the alignment invariance problem. [PERSON] et al. and [PERSON] et al. [17; 18] used graph convolution to process point clouds. Graph convolution has a powerful local feature extraction ability and can solve structural problems in point clouds, but the structure of the network is relatively complex and computationally inefficient. [PERSON] et al. [19] combined PointNet with an RNN and introduced the contextual information of the point cloud, which enabled the network to achieve more accurate classification.