SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

TL;DR

SegmentAnyTree is a versatile deep learning model for tree crown segmentation in lidar data, effective across various platforms and data densities, improving accuracy and transferability in forest analysis.

Contribution

The paper introduces a sensor-agnostic deep learning model based on PointGroup architecture that outperforms existing methods across diverse lidar datasets and data densities.

Findings

Enhanced performance with point cloud sparsification

Effective in dense forests with >50 points/m²

Outperforms existing methods like Point2Tree and TLS2trees

Abstract

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.