Coarse-to-Fine Separation of Wood and Leaf from MLS Street Tree Point Clouds Using Branch Tilt Prior and Enhanced Shortest Path Tracing

Trees play a crucial role in promoting green, ecological, and low-carbon cities, with street trees being essential for urban roadways. Understanding the 3-D structure and biological characteristics of these trees requires accurate separation of wood and leaf. Mobile laser scanning (MLS) technology, known for its high efficiency and resolution, offers significant advantages. MLS data, however, often contain missing or overlapping areas due to occlusions and scanning geometry, complicating precise urban tree modeling. To address these challenges, this article introduces a coarse-to-fine approach for distinguishing wood from leaves in urban street trees. The proposed method begins with a hierarchical workflow that integrates the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify individual tree nodes. These nodes form the basis for constructing a graph structure for each tree. By leveraging prior knowledge of branch tilt angles, we enhance the shortest path algorithm, facilitating the extraction of features like shortest path frequency and length. This initial step completes a coarse differentiation between wood and leaves. To further refine accuracy, the identified wood and leaf points undergo analysis to extract multiscale geometric features. Integrating these features with the random forest (RF) algorithm results in a more precise separation of wood and leaf points. Our method demonstrates promising segmentation capabilities in MLS-captured roadside trees. Compared to four state-of-the-art methods for wood and leaf separation, our approach shows superior accuracy and efficiency, particularly in accurately identifying trunk points and minor branch points, as well as classifying the outer canopy layer.