  This research advances individual tree crown (ITC) segmentation in lidar
data, using a deep learning model applicable to various laser scanning types:
airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge
of transferability across different data characteristics in 3D forest scene
analysis. The study evaluates the model's performance based on platform (ULS,
MLS) and data density, testing five scenarios with varying input data,
including sparse versions, to gauge adaptability and canopy layer efficacy. The
model, based on PointGroup architecture, is a 3D CNN with separate heads for
semantic and instance segmentation, validated on diverse point cloud datasets.
Results show point cloud sparsification enhances performance, aiding sparse
data handling and improving detection in dense forests. The model performs well
with >50 points per sq. m densities but less so at 10 points per sq. m due to
higher omission rates. It outperforms existing methods (e.g., Point2Tree,
TLS2trees) in detection, omission, commission rates, and F1 score, setting new
benchmarks on LAUTx, Wytham Woods, and TreeLearn datasets. In conclusion, this
study shows the feasibility of a sensor-agnostic model for diverse lidar data,
surpassing sensor-specific approaches and setting new standards in tree
segmentation, particularly in complex forests. This contributes to future
ecological modeling and forest management advancements.
