用多光谱激光雷达+深度学习实现高精度3D地表分类，助力国家测绘标准
3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes
- 构建两级地表分类体系，融合多光谱激光雷达与深度学习模型
- 点云分割准确率达79.4%（8类）和58.9%（20类），多光谱信息提升7.8个百分点
- 公开新数据集，支持国内外统一地表分类研究与标准演进
土地利用与土地覆盖（LULC）分类对三维国家地图、地理空间分析和可持续规划至关重要。多光谱（MS）LiDAR可同步提供空间与光谱信息，深度学习（DL）能实现点云语义分割；但受限于缺乏与国家测绘与地籍机构（NMCAs）分类体系对齐的公开城市及郊区多光谱激光雷达数据集，实际应用受阻。本研究提出L1和L2两级对齐NMCA标准的LULC分类方案，并发布新基准多光谱激光雷达数据集。评估七种先进深度学习模型并进行光谱消融实验。结果表明，Point Transformer V3表现最佳，使用双波长激光雷达系统（532 nm 和 1064 nm）时，在L1（8类）和L2（20类）分别取得79.4%和58.9%的平均交并比（mIoU）。消融实验显示，多光谱信息相比仅几何输入提升显著：L1提升1.1个百分点，L2提升7.8个百分点。结果凸显激光雷达反射率在细粒度材料识别中的价值，支持NMCA LULC分类体系向更高语义层级演进。Loosdorf-MSL数据集为一致性的国家与国际地表分类提供新基准。
原文摘要 · Abstract (English)
Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learning (DL) enables 3D point cloud semantic segmentation; however, adoption is limited by the lack of publicly available urban and suburban MS LiDAR datasets aligned with National Mapping and Cadastral Agencies (NMCAs) classification schemes. This study addresses these gaps by introducing L1 and L2 NMCA-aligned LULC classification schemes and a new benchmark MS LiDAR dataset. We evaluate seven state-of-the-art DL models and perform spectral ablation studies at both levels of detail. Results show that Point Transformer V3 achieves the best performance, with mIoU of 79.4% (L1, 8 classes) and 58.9% (L2, 20 classes) using a dual-wavelength LiDAR system (532 nm and 1064 nm). Ablation results show that multispectral information improves performance over geometry-only inputs, with gains of 1.1 percentage points at L1 and 7.8 points at L2. These results highlight the value of LiDAR reflectance for fine-grained material discrimination and support the evolution of NMCA LULC schemes toward higher semantic detail. The Loosdorf-MSL dataset contributes a new benchmark for consistent national and international LULC mapping.
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