Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail (LoD) in realistic 3D representation and smart cities. This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS. This paper introduces a novel method for deriving multi-LOD models, which can enhance the efficiency of spatial computing in complex 3D building models. Firstly, we extract multiple facades from a 3D building model (LoD3) and convert them into individual semantic facade models. Through the utilization of the developed facade layout graph, each semantic facade model is then transformed into a parametric model. Furthermore, we explore the specification of geometric and semantic details in building facades and define three different LODs for facades, offering a unique expression. Finally, an innovative heuristic method is introduced to simplify the parameterized facade. Through rigorous experimentation and evaluation, the effectiveness of the proposed parameterization methodology in capturing complex geometric details, semantic richness, and topological relationships of 3D building models is demonstrated.