Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion

Abstract

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.

A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm

“…To fully harness the potential of mobile laser scanning (MLS), one of the primary focuses of this research lies in the construction of a tree trunk detection dataset for deep learning. In previous studies [20,23,25,33], the datasets used for forest point cloud segmentation predominantly comprised entire forest trees. While not all researchers regard direct segmentation of complete forest scenes as unacceptable, reducing the quantity of non-target point clouds is vital to enhancing segmentation efficiency and accuracy.…”

“…In this study, we used the improved WCF-CACL-RandLA-Net model, which is suitable for large-scale point cloud segmentation, for tree trunk detection. Compared to other deep learning models [23][24][25][26][27], the RandLA-Net model employs a random sampling (RS) method that can process one million points at a time, and through the LFA structural design, it achieves aggregation of local features. This overcomes the problem of insufficient sparse keypoints information caused by RS.…”

“…This evidence corroborates the notion that CACL accelerates the convergence of deep learning models and exerts a propitious influence on segmentation precision. Similar to other tree trunk detection segmentation methods [8,9,15,16], irregular trunk shapes and point cloud scarcity caused by obstacles and laser scanning distance in the forest remain the primary factors affecting accurate measurements [23,31,32]. To determine the impact of scanning distance on ER, we divided trees into four distance levels (0-10 m, 10-20 m, 20-30 m, and 30-45 m) based on the horizontal distance between the trees and the scanning path (Figure 10) and compared the ER values within different distance levels.…”

“…Computers based on deep learning technology possess strong memory and learning capabilities [16,[20][21][22][23], effectively addressing the limitations of traditional DBH estimation methods in extracting forest DBH information, such as their difficulty in point cloud noise recognition, insufficient robustness, and low extraction efficiency. By learning trunk features, repeatable and scalable trunk recognition and localization tasks with large amounts of data can be achieved.…”

“…By learning trunk features, repeatable and scalable trunk recognition and localization tasks with large amounts of data can be achieved. Currently, mainstream deep learning methods such as Point-Net [23,24], KPConv [25], DCNN [26], and PointCNN [27] have been applied to point cloud segmentation. Most of these methods use the farthest point sampling (FPS) to retain point cloud spatial features as much as possible, but the processing speed is slow for larger datasets (1.0 × 10 5 and above).…”