A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Abstract

Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.

Deep learning based approaches from semantic point clouds to semantic BIM models for heritage digital twin

“…In the future based on the integrated data and the semantics, the heritage site digital twin platform is to provide comprehensive services, such as accuracy evaluation and process automation in BIM Modeling, site reconstruction monitoring, real time spatial analysis, AI powered generative building design and user behavior monitoring and analysis by incorporating VR. Technically, the platform is based on Cesium [57], a library that provides rapid pipelines for creating 3D Tiles from diverse data sources for massive geospatial 3D data streaming and rendering [63]. Cesium also supports interactive end-user applications, making user behavior monitoring and data collection practical.…”