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This study investigates the impact of self-supervised learning (SSL) on the robustness of leaf-wood segmentation in tree point clouds, addressing the variability in accuracy across different forest types and sites. By pretraining the Point-M2AE architecture on a diverse dataset of tree point clouds, the authors achieved significant improvements in segmentation accuracy, with wood Intersection over Union (IoU) increasing from 60.5% to 70.0% for needleleaf and from 69.7% to 76.3% for broadleaf trees. Additionally, the pretrained model demonstrated superior generalization across sites and scales, achieving the lowest error in wood volume estimation in tropical forests, indicating that SSL can enhance both segmentation performance and downstream applications.
Self-supervised pretraining not only boosts segmentation accuracy but also enables consistent performance across diverse forest types and scales.
The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites. Self-supervised learning (SSL) on point clouds has improved the generalization of deep learning models for forestry point cloud tasks, including biomass regression and individual tree segmentation, but its applicability to leaf-wood segmentation remains untested. In this study, we pretrained Point-M2AE, a widely used SSL architecture for point clouds, on ShapeNet-55 augmented with 2,400 individual tree point clouds. For fine-tuning and inference, we used recursive voxel subdivision to handle the wide variation in point density across inputs, allowing the same model to operate at both individual-tree and plot scales without architecture change. Compared to the model without pretraining, the pretrained model improved wood IoU from 60.5% to 70.0% for needleleaf and from 69.7% to 76.3% for broadleaf trees. On a benchmark spanning four countries across three climatic zones, the pretrained model achieved the smallest cross-site variation and highest overall performance among compared methods (LeWos, CWLS, and PointTransformer). Plot-level segmentation maintained accuracy comparable to individual-tree performance, with mIoU of 84.7% for broadleaf and 77.7% for needleleaf plots, showing that the model generalizes across scales without additional finetuning. As a downstream test in tropical forests, where dense canopies make segmentation challenging, we applied our model and a quantitative structure model to estimate wood volume for 28 trees from Guyana, Indonesia, and Peru to assess whether the segmentation improvements from SSL pretraining translate into improved downstream performance. The resulting volume estimates achieved the lowest error among all methods tested (MAE = 2.40 m$^3$), less than half that of algorithmic baselines (LeWos: 5.94 m$^3$; CWLS: 5.27 m$^3$).
