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Semantic 3D building models are the basic data of various applications for smart urban management. However, it is still a challenge to produce semantic models for all the buildings in a specified large-scale area through a stable and reliable process. In this paper, a general and effective scheme is introduced for semantic 3D building model construction and some example applications are illustrated to demonstrate the great capability of semantic 3D building models in different aspects. KEYWORD: Semantic modeling; 3D building model; Urban management; Smart city International Conference on Industrial Technology and Management Science (ITMS 2015) © 2015. The authors Published by Atlantis Press 569 3.1 Geometric modeling Rapid development of survey and mapping technology makes it much easier to generate highresolution models in a large-scale area. A lot of cities and districts have been working on digital city construction, where air-borne and mobile Lidar (Light detection and ranging) devices are employed to collect accurate positions of every part of urban objects and construct detailed 3D models (see Fig.1). Figure 1. High-resolution geometric building model construction with Lidar data. For 3D geometric modeling of buildings, it is proposed to extract the façades from point cloud firstly (Yang et al. 2012), and make reasonable organization of them according to some rules (Nan et al. 2010, Furukawa et al. 2010). 3.2 Structural part extraction Structure is the main characteristics that distinguish man-made objects from natural objects, which reflect the relationships between compositional parts of objects. Since the semantic information always manifest as special patterns in the physical world, it is believed that there’re inherent connections between geometric structures and semantics. Thus, structural part extraction is the key of semantic 3D building model construction. As to the structural part recognition of 3D building models, we consider both the macro level parts and detailed parts. The macro level parts are the overall components of building models (Sun et al. 2011a, b), such as the roofs, annex, and different floors, whereas the detailed parts are the basic components of building models (Li et al. 2013), such as the balconies, windows, doors, and chimneys.  Macro level structural part extraction: volume decomposition algorithms are employed to partition the building model in 3D space, so that the overall compositional parts of the model can be recognized (see Fig. 2 for example).  Detailed structural part extraction: surface segmentation or geometric analysis algorithms are employed to extract specific features from the outer surface of the building model, so that the tiny visible features can be recognized (see Fig. 3 for example). Figure 2. Macro level structural part recognition for 3D building models. Figure 3. Detailed structural part recognition for 3D building models. 3.3 Semantic modeling After the structural part recognition, semantic 3D building model can be constructed, where the semantic information are organized hierarchically (Zhu & Hu 2008). The top level records the spatial composition of the building, whereas the bottommost level records all the functional parts. The relationship between higher semantic parts and lower ones is built according to the geometric ownership between them (see Fig. 4).

Semantic 3D Building Model Construction for Smart Urban Management