# Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data
## 1 Introduction  
Intelligent transportation unmanned autonomous driving urgently needs high-precision navigation maps, and obtaining information on the location and details of feature targets in complex road scenes through telemetry is an important support for achieving accurate positioning and navigation. In traffic management, the 3D geometric, topographical and semantic information of trees in road scenes has certain reference value for fine lane navigation, traffic accident analysis and vehicle safety assessment. For the construction of high-precision navigation maps and intelligent traffic management needs, fast and accurate extraction of relevant features in the driving environment is the current research hot spot in the construction of \"smart cities\". However, at the data source level, targets such as trees with large differences between top and bottom geometric features cannot be extracted finely by deciphering traditional single 2D images, and the current mainstream tree recognition extraction method uses 3D LiDAR (Light Detection and Ranging) as the data source. LiDAR technologyhas gradually become one of the important data sources for earth observation and target classification and identification with its ability to acquire information about the surrounding environment quickly and in real time. LiDAR is a high-precision sensor for measuring the position and shape of objects and forming high-quality 3D point cloud images [1,2], and it has been widely used in recent years for autonomous driving [3, 4, 5], 3D modeling [6, 7, 8, 9], generating high-precision maps [10, 11], tree biomass estimation [12, 13] and other fields. Street tree extraction is divided into airborne LiDAR and ground-based LiDAR (including vehicle-mounted, backpack and base station types) depending on the LiDAR data source. Airborne LiDAR point clouds are characterized by rapid acquisition and large geographical coverage; therefore, airborne LiDAR is mainly used for tree information extraction and biomass estimation, reducing data costs while preserving the extent of individual tree canopies and facilitating simplified tree population statistics extraction and biomass estimation. The ground-based LiDAR point cloud can capture the geometric information of trees with high accuracy in all directions, facilitating the extraction of salient features of individual trees to obtain accurate tree parameters for road environment refinement management and stock estimation. Currently, tree classification extraction based on LiDAR point cloud data consists of two parts: extraction of trunks and extraction of crowns. (1) Trunk extraction methods can be classified as: model-based extraction, structural feature-based extraction and knowledge template matching-based extraction methods. 1) Model-based extraction method: the tree trunk presents a cylindrical-like geometry. [PERSON] et al. [14,15] and [PERSON] et al. [16] applied a 3D cylindrical model to extract the trunk; [PERSON] et al. [17] applied coaxial cylinders of different size radii to fit the trunk; [PERSON] et al. [18] applied an adaptive radius cylindrical model to fit the trunk from the bottom up; [PERSON] et al. [19] and [PERSON] et al. [20] used a columnar surface fitting approach; and there are also a number of methods that apply RANSAC (random sample consensus) linear fitting models based on verticality [21, 22, 23]. However, such methods for fitting trunks are strongly influenced by the density and geometry of trunk points, and regular model-fitting methods cannot extract the trunk completely when it is obscured by other types of feature points (e.g., bushes) or when the trunk is tilted to a large extent. 2) Extraction methods based on structural features: tree trunks have rod-like features with high verticality, density and continuity. Many studies have used clustering and segmentation methods to extract trunks based on structural features. [PERSON] et al. [24] combined a priori knowledge of tree height, location and number of points to cluster and separate trunks; [PERSON] et al.