SynthRoads: A framework for comprehensive enrichment of the Scan-to-Twin process for road space modeling with synthetic data

Digital twins of the built environment promise major efficiency gains in managing and maintaining infrastructure. A main component of Digital Twin systems is an information-rich, up-to-date, virtual representation of the physical scene. Deriving such an as-is geometric–semantic model (GSM) of the target environment that is both timely and realistic poses a substantial challenge. In particular, the required data aggregation and processing remain substantial obstacles for robust and highly automated generation. The Scan-to-Twin workflow aims to automatically produce an as-is GSM from reality-capture data, but deep learning–based automation still depends on costly manual annotations. We present SynthRoads, a framework that synthetically generates multi-modal data for every step of Scan-to-Twin, enabling deep learning for point-cloud semantic segmentation and geometric extraction in road environments. SynthRoads combines modular model generation with synthetic laser scanning to efficiently create annotated point clouds, geometry parameters, and GSMs. While broadly applicable, we demonstrate significant performance gains for road semantic segmentation and road centerline regression in Scan-to-Twin use cases. Our framework is highly adaptable, leveraging the realism of a game engine and a procedural modeling approach to efficiently generate diverse and realistic road scenes.