A Local– Global Feature Aggregation Method for Semantic Segmentation of Lidar Point Clouds in Railway Scenarios

Railways serve as critical national infrastructures, and anomalies in key facilities can pose serious threats to transportation safety. Due to complex spatial structures and large-scale variations in railway point clouds, existing semantic segmentation methods struggle with multi-scale feature representation and semantic modeling. To address this issue, we propose a graph convolution–based point cloud semantic segmentation method. The network uses a multi-level feature aggregation framework in which dilated residual blocks facilitate adaptive feature fusion, and hierarchical downsampling progressively expands the receptive field to capture global structural information. Furthermore, a coordinate-guided graph convolution network is introduced to enhance local structural perception and encode topological relationships by constructing graphs from point coordinates and propagating features to model geometric relations and long-range dependencies. Together, these components achieve a unified multi-scale semantic representation for railway point clouds. With the WHU-Railway3D data set, our method achieved mean intersection over union scores of 72.40% and overall accuracy of 91.10% in the urban scenarios and mean intersection over union scores of 77.89% and overall accuracy of 95.50% in the rural scenarios, which shows state-of-the-art performance among current methods.