Unsupervised Class-Guided Synthetic-to-Real Domain Adaptation for Plot-Level Forest UAV LiDAR Semantic Segmentation

Abstract

Semantic segmentation of forest point clouds is pivotal for automated forest inventory but is severely constrained by the scarcity of high-quality real-world annotations. Although synthetic forest point clouds provide a scalable alternative for training, models trained on synthetic data often suffer from pronounced performance degradation when transferred to real forest scenes, due to domain shifts coupled with extreme structural heterogeneity and long-tailed semantic distributions. To address these challenges, we propose ForestPlotUDA, an unsupervised domain adaptation (UDA) framework specifically designed for plot-level forest UAV light detection and ranging (LiDAR) semantic segmentation. Unlike generic adaptation methods that rely on global feature alignment or dense pseudo-labeling, ForestPlotUDA explicitly targets the severe class imbalance and plot-wise structural variability inherent in forest environments, where ecologically critical woody components are sparsely distributed and easily overwhelmed by dominant foliage points. The proposed framework integrates decoupled feature normalization and class-guided self-training, enabling stable cross-domain adaptation under highly imbalanced and sparse supervision. Experimental results on the FOR-instance benchmark demonstrate that ForestPlotUDA improves the mean intersection-over-union (mIoU) from 54.71% to 61.50%. Notably, using only five pseudo-labeled points per class, the intersection-over-union (IoU) of the challenging wood class increases from 22.82% to 40.90%, highlighting the effectiveness of the proposed approach for annotation-free forest UAV LiDAR analysis. Additional experiments on SegmentedForests further demonstrate that ForestPlotUDA consistently improves over direct synthetic-to-real (Syn2real) transfer on ground-based terrestrial laser scanning (TLS)/mobile laser scanning (MLS) forest point clouds. These results indicate that explicitly accounting for structural heterogeneity and class imbalance is critical for Syn2Real adaptation in forest point clouds, paving the way for fully automated and low-cost forest inventory systems across diverse forest ecosystems.