# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 1 Introduction
[25] used trunk orientation and geometric information to segment trunks; [PERSON] et al. [26] used multi-scale shape and size geometric features to achieve extraction; [PERSON] et al. [27] applied DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to distinguish trunks by density; [PERSON] et al. [28] extracted horizontal deviations of points from centroids in a hierarchical manner in the z-direction; and [PERSON] et al. [29] searched for upper and lower voxels over a range of heights to achieve voxelized trunk extraction. [PERSON] et al. [30] applied trunk points using horizontal profile analysis and minimum vertical height criterion extraction, and [PERSON] et al. [31] applied Euclidean distance clustering and minimum cut extraction. The computation of structural features depends on the sparsity of neighborhoods points and is influenced by noisy points, and the extraction accuracy of structural feature-based extraction methods is low for small target trunks and incomplete irregular trunks. [PERSON] et al. [32] proposed a street tree extraction and segmentation method based on the spatial geometric features of object primitives, applying a normal vector angle threshold between neighborhood points to divide the original point cloud into different primitives, and extracting trunk points through the linear features between primitives and the aspect ratio of the primitive bounding box, which can improve the robustness and accuracy of stem detection, but there is room for improvement in the integrity performance of stem detection for some irregular stems, such as the detection of severely curved trunk points. 3) Extraction methods based on knowledge template matching: knowledge templates are combinations of features, and threshold combinations of different feature values can extract different features, separating tree trunks based on the combined feature differences between tree trunks and other feature targets. [PERSON] et al. [33] achieved feature classification based on knowledge-based feature recognition, and super voxels are a feature fusion method; [PERSON] [29] et al. and [PERSON] [34] et al., in the segmentation process, applied feature hyper voxels of tree trunks; [PERSON] [21, 35] combined prior knowledge, pole extraction and component separation to distinguish trees from other features; and [PERSON] et al. [36] applied the Hough forest of hyper voxel neighborhoods to detect pole-like features including tree trunks. Voxelization is often applied to template matching methods, but the choice of size of voxelization is critical to the extraction results. Moreover, this method requires a large amount of a priori knowledge, and the processing is time consuming. (2) The acquisition of a single tree canopy point cloud can be divided into two aspects: classification and extraction. 1) Methods for canopy classification: The canopy layer of trees is a distinct part that distinguishes them from other features and has significantly different features from other features in the application of machine learning classification methods. [PERSON] et al. [37] used Euclidean clustering for point cloud coarse processing and then applied SVM to extract single trees; [PERSON] and [PERSON] [38] used statistical pole description to conform to the application of SVM for different pole features (including trees, streetlights, billboards, etc. ); [PERSON] et al. [39] used super voxel processing and then applied SVM and random forest processing to classify different pole features, respectively, as well as applying 3D convolutional neural networks for large-field point cloud classification [40]. The machine learning approach requires a large amount of manual labeling and training, is suitable for target classification in large scenes, is time consuming and has low extraction accuracy when targeting specified features, and the extraction accuracy depends on the sample.