The digitisation of existing railway geometry from point clouds referred to as “twinning” is a laborious task; currently outweighing the perceived benefits of the resulting model. State-of-the-art methods have provided promising results, yet they cannot offer large-scale rail class segmentation requires over kilo-metres without forfeiting precision and labour cost. The authors exploit the potential benefits of railway topology to automate the twinning process. The preliminary step is automatically segmenting mast point clusters as their positions are critical for the subsequent railway assets’ class segmentation. The proposed method first removes vegetation and noise; then segments masts using the RANSAC algorithm relative to the track centerline, and delivers final models in IFC format. The authors validated the method on 18 km railway point cloud and yielded an overall segmentation accuracy of 90.1% F1 score. The proposed method lays foundations to efficiently generate geometry-only digital twins of railway assets with no prior information.