Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds

Segmenting individual trees from airborne LiDAR point cloud data is critical for forest management, urban planning, and ecological monitoring but remains challenging due to complex natural environments, diverse tree architectures, and dense canopies. Traditional supervised methods rely on extensive, manually annotated datasets that are often impractical to obtain. In this study, we propose a novel self-supervised learning framework that eliminates the need for manual labeling by integrating transformation-invariant feature extraction, an energy-based segmentation loss, and soft clustering. The framework operates in two stages: a pretext task applies geometric transformations—rotation (from –45° to +45°), translation (between –1 and 1 units), and scaling (between 0.5 and 2.0)—to learn robust features, while an unsupervised segmentation step leverages an energy function that combines height, density, and slope attributes to cluster points corresponding to individual trees. We evaluated our approach on a high-density LiDAR dataset acquired from the Estonian Land Board (LAS format, version 1.4) comprising over 850,000 points. Our method achieves up to 87% convexity, 78% solidity, and an elliptical fit error as low as 0.12, substantially reducing over-segmentation compared to baseline clustering techniques. These results demonstrate that our self-supervised framework offers a scalable, label-free solution for precise tree segmentation, with significant advantages in accuracy and efficiency over traditional supervised methods.