RailDLA-Net: An Intensity-Aware Deep Local Aggregation Framework for Railway Point Cloud Semantic Segmentation

Accurate semantic segmentation of railway point clouds is essential for intelligent railway inspection and infrastructure management. However, railway scenes contain elongated structures, locally similar objects, and severe class imbalance, while the discriminative value of LiDAR intensity is still insufficiently exploited. To address these issues, this study proposes RailDLA-Net, an intensity-aware deep local aggregation framework for railway point cloud semantic segmentation. The network adopts an encoder–decoder architecture and integrates a railway structure-guided residual local encoding module to capture local geometric variations, a hierarchical structural context aggregation module to model multi-level contextual dependencies, and an intensity-aware adaptive attention mechanism to enhance the discrimination of geometrically similar categories. In addition, a geometry–semantics collaborative objective is introduced to jointly optimize final predictions, intermediate semantic representations, and local spatial structures. Experiments were conducted on Rail3D and WHU-Railway3D datasets to evaluate RailDLA-Net under different railway scene conditions. The proposed method achieved 96.89% OA, 91.92% mAcc, and 88.44% mIoU on Rail3D, and 92.36% OA, 91.84% mAcc, and 83.63% mIoU on WHU-Railway3D. Compared with the baseline methods, RailDLA-Net showed competitive overall performance and improved the balance across categories, especially in more complex and imbalanced railway scenes. Ablation studies further demonstrated the effects of key configuration settings and the loss design. Together with the evaluations on two railway point cloud datasets, these results support the effectiveness of RailDLA-Net as an integrated railway-oriented framework for fine grained point cloud semantic segmentation.