An Improved Method for Individual Tree Segmentation in Complex Urban Scenes Based on Using Multispectral LiDAR by Deep Learning

Abstract

Urban trees, as a characteristic element of the urban ecosystem, exert significant influences on climate supervision. Therefore, the extraction of individual trees in urban areas holds significant research value. However, the complexity of features in urban areas poses challenges to existing single tree segmentation algorithms, as they may be influenced by other nontree features. In this study, to reduce the influence of nontree categories, enhance the identification of edge features between adjacent tree crowns, and achieve precise delineation results of the single urban tree, an improved multistage method was proposed for tree points extraction and individual tree segmentation in urban scenes using multispectral LiDAR. First, the original three single-channel point clouds were preprocessed by intensity interpolation to generate a three-wavelength multispectral point cloud. Second, the Point Transformer deep learning network was employed for extracting urban tree points. Third, an improved tree mapping algorithm was introduced for individual tree segmentation in urban scenes, utilizing the extracted tree points. Finally, manual individual tree labeling and the high-resolution digital orthophoto map of the region were incorporated to measure the delineation precision of individual trees. It shows that the intersection over union of tree category in urban scene reaches 96.0%. Moreover, the F1-score for overall individual tree segmentation attains 92.8%. However, a comparison with existing algorithms reveals that the proposed method outperforms the traditional raster-based watershed method or point cloud clustering-based layer-stacking approach in the urban scene, improving the overall accuracy of single tree segmentation by 21.9% and 16.0%, respectively. These results highlight the enhanced applicability of the proposed multistage algorithm for urban scenes.