Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores the use of Point Transformer v2 (PTv2), an advanced deep neural network architecture designed for point cloud data, for land cover mapping through semantic segmentation of multispectral LiDAR data in real-world riverine environments. We utilize the geometric and spectral information from the 3-channel LiDAR point cloud to map land cover classes, including sand, gravel, low vegetation, high vegetation, forest floor, and water. The PTv2 model was trained and evaluated on point cloud data from the Oulanka river in northern Finland using both geometry and spectral features. To improve the model's generalization in new riverine environments, we additionally investigate multi-dataset training that adds sparsely annotated data from an additional river dataset. Results demonstrated that using the full-feature configuration resulted in performance with a mean Intersection over Union (mIoU) of 0.950, significantly outperforming the geometry baseline. Other ablation studies revealed that intensity and reflectance features were the key for accurate land cover mapping. The multi-dataset training experiment showed improved generalization performance, suggesting potential for developing more robust models despite limited high-quality annotated data. Our work demonstrates the potential of applying transformer-based architectures to multispectral point clouds in riverine environments. The approach offers new capabilities for monitoring sediment transport and other river management applications.

翻译：准确的河流环境土地覆盖制图对于有效的河流管理、生态理解及地貌变化监测至关重要。本研究探索了使用Point Transformer v2 (PTv2)——一种专为点云数据设计的高级深度神经网络架构——通过语义分割多光谱LiDAR数据在实际河流环境中进行土地覆盖制图。我们利用三通道LiDAR点云的几何与光谱信息，对包括沙地、砾石、低矮植被、高大植被、林下地表及水体在内的土地覆盖类别进行映射。PTv2模型基于芬兰奥兰卡河的点云数据，结合几何与光谱特征进行训练与评估。为提升模型在新河流环境中的泛化能力，我们进一步研究了多数据集训练方法，该方法补充了来自另一河流数据集的稀疏标注数据。结果表明，采用全特征配置时，模型平均交并比（mIoU）达到0.950，显著优于仅基于几何特征的基线模型。其他消融实验揭示，强度与反射率特征是实现精确土地覆盖制图的关键。多数据集训练实验展现了泛化性能的提升，表明在有限高质量标注数据条件下仍可开发更稳健的模型。本工作证明了基于Transformer架构应用于河流环境多光谱点云的潜力。该方法为监测沉积物迁移及其他河流管理应用提供了新能力。