BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach
This provides automated insights into tree growth and wood quality for forestry applications, but it is incremental as it applies existing pose estimation methods to a new domain.
The paper tackled the problem of detecting tree whorls in laser scanning data using a deep learning pose estimation model, resulting in accurate identification and precise calculation of structural metrics from point clouds.
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain. The workflow processes point cloud data to create sectional images, which are subsequently used to identify keypoints representing tree whorls and branches along the stem. The method was tested on a dataset of destructively sampled individual trees, where the whorls were located along the stems of felled trees. The results demonstrated strong potential, with accurate identification of tree whorls and precise calculation of key structural metrics, unlocking new insights and deeper levels of information from individual tree point clouds.