Cross-platform forest understanding: A multi-platform synergistic training framework for generalized forest point cloud segmentation

Abstract

Precise forest inventories are fundamental for sustainable ecosystem management, biodiversity conservation, and assessment of forest carbon stocks. Light Detection and Ranging (LiDAR) has emerged as a dominant remote sensing technique capable of accurately characterizing the vertical canopy profile over large areas. While the growing availability of LiDAR datasets from diverse acquisition platforms has driven significant advances in forest structural component and individual tree segmentation tasks, inherent heterogeneity in data characteristics across multiple platforms poses a critical challenge for cross-platform generalization. Traditional data-driven algorithms often fail to generalize across multi-platform forest datasets due to substantial heterogeneity in data characteristics from different acquisition platforms. Furthermore, conventional multi-platform mixed training strategies induce negative transfer, ultimately undermining segmentation performance. To address the inherent heterogeneity and negative transfer challenges arising from multi-platform datasets, we introduce the Multi-platform Synergistic Training (MST) framework, a unified, data- and model-driven representation learning framework. This framework initially pretrains with virtual synthetic forest datasets to extract generalized feature representations, followed by platform-specific fine-tuning utilizing real-world datasets. Extensive experiments were conducted to evaluate the proposed MST on semantic and instance segmentation tasks using forest benchmark datasets from multiple platforms. The results reveal that the MST achieves consistently high segmentation performance across multi-platform datasets (from airborne, unmanned aerial vehicle, mobile and terrestrial laser scanning). Additionally, leveraging MST pretraining enables the use of only 20% of labeled real-world data to match the segmentation accuracy achieved by training on fully annotated datasets. The MST framework therefore represents a powerful and effective representation learning framework with the potential to support downstream forest inventory and ecological applications.