PDEC-Net: An Improved Single Tree Segmentation Method for Urban Mobile Laser Scanning Point Clouds Based on PDE-Net

This study focuses on the task of single tree segmentation in urban environments and improves the PDE-Net model to enhance its ability to process complex features. The original PDE-Net model has limitations when dealing with trees in a large area of land use. In this study, we propose an improved model, PDEC-Net, which combines the original model with the Encoder-Decoder structure, and introduces the Depthwise Separable Convolution layer to better capture the global and local relationships of point clouds. The experimental results show that the PDEC-Net model outperforms the original model in evaluation indicators such as Pre, Recall, and F1 score. Compared with the original PDE-Net, the PDEC-Net has increased Pre, Recall, and F1 score by 4.2%, 0.6%, and 3.2%, respectively. Meanwhile, the improved model also shows better adaptability when dealing with large-scale test sets. Therefore, the PDEC-Net model demonstrates stronger performance in single tree segmentation tasks, providing more powerful support for urban planning, management, and environmental monitoring.