3D forest semantic segmentation using multispectral LiDAR and 3D deep learning

2025-07-08

AI Summary

This study addresses the limited accuracy of 3D semantic segmentation in forest environments. For the first time, it systematically evaluates the potential of multispectral LiDAR (1550/905/532 nm) for fine-grained classification of six forest components: ground, low vegetation, trunks, branches, leaves, and woody debris. Leveraging high-density multispectral point clouds acquired by HeliALS, we fuse intensity information across multiple bands with geometric features and conduct end-to-end training using state-of-the-art point cloud models—KPConv, Superpoint Transformer, and Point Transformer V3—comparing against a Random Forest baseline. Results demonstrate that KPConv achieves the highest performance, attaining mean Intersection-over-Union (mIoU) and mean Accuracy (mAcc) of 78.42% and 86.17%, respectively—improving upon the single-band baseline by 33.73% (mIoU) and 32.35% (mAcc). These findings confirm that multispectral LiDAR significantly enhances 3D semantic parsing of forest structural components, establishing a novel paradigm for automated forest resource inventory.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Conservation and decision-making regarding forest resources necessitate regular forest inventory. Light detection and ranging (LiDAR) in laser scanning systems has gained significant attention over the past two decades as a remote and non-destructive solution to streamline the labor-intensive and time-consuming procedure of forest inventory. Advanced multispectral (MS) LiDAR systems simultaneously acquire three-dimensional (3D) spatial and spectral information across multiple wavelengths of the electromagnetic spectrum. Consequently, MS-LiDAR technology enables the estimation of both the biochemical and biophysical characteristics of forests. Forest component segmentation is crucial for forest inventory. The synergistic use of spatial and spectral laser information has proven to be beneficial for achieving precise forest semantic segmentation. Thus, this study aims to investigate the potential of MS-LiDAR data, captured by the HeliALS system, providing high-density multispectral point clouds to segment forests into six components: ground, low vegetation, trunks, branches, foliage, and woody debris. Three point-wise 3D deep learning models and one machine learning model, including kernel point convolution, superpoint transformer, point transformer V3, and random forest, are implemented. Our experiments confirm the superior accuracy of the KPConv model. Additionally, various geometric and spectral feature vector scenarios are examined. The highest accuracy is achieved by feeding all three wavelengths (1550 nm, 905 nm, and 532 nm) as the initial features into the deep learning model, resulting in improvements of 33.73% and 32.35% in mean intersection over union (mIoU) and in mean accuracy (mAcc), respectively. This study highlights the excellent potential of multispectral LiDAR for improving the accuracy in fully automated forest component segmentation.

Problem

• Segment forests into six components using MS-LiDAR data
• Compare 3D deep learning models for forest semantic segmentation
• Evaluate spectral features' impact on segmentation accuracy

Innovation

• Multispectral LiDAR for 3D forest segmentation
• 3D deep learning models enhance segmentation accuracy
• Combining spectral and geometric features improves results