Abstract
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.
Abstract (translated)
在河流环境中进行精确的土地覆盖制图，对于有效的河流管理、生态理解以及地貌变化监测至关重要。本研究探索了使用专为点云数据设计的先进深度神经网络架构——Point Transformer v2（PTv2），通过真实河流环境中多光谱LiDAR数据的语义分割来实现土地覆盖制图。我们利用3通道LiDAR点云的几何与光谱信息，对包括沙地、砾石、低植被、高植被、林下层和水体在内的土地覆盖类别进行制图。该PTv2模型基于芬兰北部奥兰卡河的几何与光谱特征点云数据进行了训练与评估。为提升模型在新河流环境中的泛化能力，本研究进一步探究了通过添加另一河流数据集中稀疏标注数据的多数据集训练方法。结果表明，采用全特征配置的模型性能达到平均交并比（mIoU）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。多数据集训练实验显示出改进的泛化性能，表明即使在高质量标注数据有限的情况下，也有潜力开发出更稳健的模型。我们的工作证明了基于Transformer的架构应用于河流环境多光谱点云的潜力，该方法为监测泥沙输运及其他河流管理应用提供了新的能力。
URL
https://arxiv.org/abs/2603.22230
