The cost of the railway digital twinning process counteracts the expected benefits of the resulting model. State-of-the-art methods yielded promising results, yet they could not offer large-scale digital twinning required over kilometres without forfeiting precision and manual cost. The proposed framework exploits the potential of railway topology to perform better when detecting and modelling the geometry of railway elements in railway point clouds with varying geometric patterns. Experiments on 18 km railway datasets illustrate that the framework improves the current cost and benefit ratio by reducing the overall twinning time by 90% without using any prior information.