用多光谱激光雷达+深度学习，精准提取城市树木点云。
Multispectral LiDAR data for extracting tree points in urban and suburban areas
- 融合光谱与空间数据，提升树木点云分割精度。
- SPT模型达到85.28%的平均交并比，效率与准确率俱佳。
- 引入伪归一化植被指数可降低10.61%误差，适合城市绿化管理。
监测城市树木动态对支持绿化政策和降低电力设施风险至关重要。机载激光扫描已推动大规模树木管理，但复杂的城市环境和树木多样性仍带来挑战。多光谱激光雷达（MS-LiDAR）通过同时获取三维空间与光谱数据，实现更精细的制图。本研究探索利用MS-LiDAR与深度学习模型进行树木点提取，评估了三种先进模型：Superpoint Transformer（SPT）、Point Transformer V3（PTv3）和Point Transformer V1（PTv1）。结果表明，SPT在时间和精度上表现优异，平均交并比（mIoU）达85.28%。结合伪归一化差值植被指数（pNDVI）与空间数据，检测准确率显著提升，相较仅使用空间信息降低10.61个百分点的误差率。研究证实，MS-LiDAR与深度学习技术在提升树木提取精度和后续树种普查方面具有巨大潜力。
原文摘要 · Abstract (English)
Monitoring urban tree dynamics is vital for supporting greening policies and reducing risks to electrical infrastructure. Airborne laser scanning has advanced large-scale tree management, but challenges remain due to complex urban environments and tree variability. Multispectral (MS) light detection and ranging (LiDAR) improves this by capturing both 3D spatial and spectral data, enabling detailed mapping. This study explores tree point extraction using MS-LiDAR and deep learning (DL) models. Three state-of-the-art models are evaluated: Superpoint Transformer (SPT), Point Transformer V3 (PTv3), and Point Transformer V1 (PTv1). Results show the notable time efficiency and accuracy of SPT, with a mean intersection over union (mIoU) of 85.28%. The highest detection accuracy is achieved by incorporating pseudo normalized difference vegetation index (pNDVI) with spatial data, reducing error rate by 10.61 percentage points (pp) compared to using spatial information alone. These findings highlight the potential of MS-LiDAR and DL to improve tree extraction and further tree inventories.
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