Towards automatic urban tree inventory: Enhancing tree instance segmentation via moving object removal and a chord length-based DBH estimation approach

Abstract

To enhance urban forestry efficacy in Hong Kong, implementing a paradigm shift towards an automated urban tree inventory that utilizes advanced sensing technologies and artificial intelligence is essential for streamlined data collection and analysis. This study advances this objective by creating a comprehensive framework for estimating diameter at breast height (DBH) and extracting tree images. This framework encompasses five key stages: (1) data acquisition utilizing StructXray, a mobile mapping system equipped with a 360° camera and a multi-beam flash LiDAR sensor; (2) vegetation point clouds extraction using deep learning techniques; (3) individual tree segmentation through machine learning algorithms; (4) DBH estimation; and (5) tree image extraction. Six datasets were collected, yielding tree detection precision, recall and F1 score of 0.88, 0.95 and 0.91 respectively. The presence of moving objects within the 3D point cloud map, exhibiting diverse geometric structures, hinders precise vegetation point cloud segmentation by the pointwise neural network. To tackle this challenge, SalsaNext was employed to rectify the predictions of a pointwise neural network, specifically RandLA-Net in this study, eliminating 91 % of misclassified moving object point clouds and completely removing them from 47 % of affected individual tree point clouds. Additionally, a chord length-based method was proposed to enhance DBH estimation accuracy by dividing the point cloud slice into sectors and summing the chord lengths to estimate the tree trunk perimeter. Compared to the ellipse least squares fitting method, this approach reduced the root-mean-square error of the estimated DBH by 1.31 cm.