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.
Peng et al. (Thu,) studied this question.