A Novel Framework for Individual Tree Segmentation in Complex Urban Forests from Terrestrial LiDAR Point Clouds

Accurate individual tree inventories are fundamental to urban forest management, yet automated delineation from Terrestrial Laser Scanning (TLS) data remains a challenge. This study presents a two-stage hybrid framework that combines a domain-adapted deep learning model (TreeLA-Net) with a geometric algorithm (SEGR) to solve this issue, aiming to reduce the need for instance-level annotations. TreeLA-Net first generates semantic labels, outperforming the baseline RandLA-Net by 2.5 percentage points in overall accuracy. Subsequently, SEGR leverages these priors to achieve a tree detection rate of 92.0% on our primary study site. To assess the framework’s transferability, an external validation was conducted on a new, independent site, where the model, without retraining, yielded a recall of 81.5%. These findings suggest that the framework is not strictly overfitted and possesses generalization capabilities. The proposed approach is offered as a potential tool to support data-driven urban forest management, particularly for automated tree mapping and inventory. We hope that this study may contribute to ongoing efforts to develop robust methods for characterizing complex urban forest structures.