## 1 Introduction  
Airborne Laser Scanning (ALS) can capture large-scale point clouds of urban scenes. The point cloud classification of outdoor scenes can provide high-precision semantic maps for autonomous driving, improve the accuracy of vehicle positioning, and reconstruct a three-dimensional model of the city, which plays an important role in urban planning and dynamic management. In addition, it can improve the efficiency of resource utilization. Effectively labeling the correct class for all points in the scene is an important basis for the widespread adoption of point clouds [1; 2; 3; 4]. However, a laser point cloud has a huge data number, high redundancy, and uneven scene distribution, which may lead to huge challenges in the point cloud classification. Therefore, it is of great significance to classify the three-dimensional point cloud in large outdoor scenes. Currently, the number of point clouds with manual labeling in outdoor large scenes is not enough. However, machine learning can learn and classify point clouds in the case of less sample training data, and the speed is faster. At present, the point cloud classification methods can be mainly divided into two strategies: the single point-based classification and object-based classification methods. The point-based point cloud classification is the classification of each individual point in a point cloud; this strategy uses points as the basic unit to extract features, train models, and predict class labels. There are three main steps: the neighborhood selection, feature extraction, and single point classification based on the features and classifiers. (1) Neighborhood selection. In the neighborhood selection process, the commonly used point cloud neighborhood forms are: K nearest neighbors [5], radius neighborhoods [6], and column neighborhoods [7]. The parameter of neighborhood estimation is highly dependent on prior knowledge, and it is greatly affected by the change of the point cloud density [8; 9]. For example, [PERSON] et al. constructed a multi-level scale pyramid, and a total of 144-dimensional features such as eigenvalues of the covariance matrix were extracted for each point in each pyramid. Subsequently, Random Forest was used for training and finally classified outdoor road scenes [10], and a better classification effect was obtained. Therefore, the selection based on multi-scale neighborhood is an important method for extracting single point effective features. (2) Features extraction. The local feature of a point cloud is an abstract depiction of the environment around a given point. It is difficult to classify a point cloud by a single local feature. The common practice is to fuse multiple point cloud local features for classification. Normal and curvature are simple local features that clearly show some local information about the point cloud, such as the fact that the normal direction can represent the partial tangent plane of the point cloud, and the curvature can represent the smoothness of the point. For example, [PERSON] et al. [11] optimized the matching accuracy of point pairs in point clouds based on the curvature information, and the registration accuracy of point clouds was further improved. Geometric features are also common local features, also known as shape descriptors. For example, in the spin image [12], the main idea is to set up an image with the normal vector as the center; the image rotates around the axis. The number of 3D points encountered by each pixel in the point cloud is taken as its gray value. Finally, a two-dimensional array representing the local information of the three-dimensional space, that is, the rotated image feature, is obtained. The 3D Shape Context (3 DSC) feature [13] is based on the specified point to construct a spherical region. In the support region, the grid is divided into three coordinate directions: the radial direction, direction angle and elevation angle. Following this, a feature histogram is constructed by entering the number of points in the grid.