Knowledge-driven semantic converting method of multimodal models toward a geospatial perspective

In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.