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Cities and urban areas entities such as building structures are becoming more complex as the modern human civilizations continue toevolve. The ability to plan and manage every territory especially the urban areas is very important to every government in the world.Planning and managing cities and urban areas based on printed maps and 2D data are getting insufficient and inefficient to cope withthe complexity of the new developments in big cities. The emergence of 3D city models have boosted the efficiency in analysing andmanaging urban areas as the 3D data are proven to represent the real world object more accurately. It has since been adopted as thenew trend in buildings and urban management and planning applications. Nowadays, many countries around the world have beengenerating virtual 3D representation of their major cities. The growing interest in improving the usability of 3D city models hasresulted in the development of various tools for analysis based on the 3D city models. Today, 3D city models are generated forvarious purposes such as for tourism, location-based services, disaster management and urban planning. Meanwhile, modelling 3Dobjects are getting easier with the emergence of the user-friendly tools for 3D modelling available in the market. Generating 3Dbuildings with high accuracy also has become easier with the availability of airborne Lidar and terrestrial laser scanning equipments.The availability and accessibility to this technology makes it more sensible to analyse buildings in urban areas using 3D data as itaccurately represent the real world objects. The Open Geospatial Consortium (OGC) has accepted CityGML specifications as one ofthe international standards for representing and exchanging spatial data, making it easier to visualize, store and manage 3D citymodels data efficiently. CityGML able to represents the semantics, geometry, topology and appearance of 3D city models in fivewell-defined Level-of-Details (LoD), namely LoD0 to LoD4. The accuracy and structural complexity of the 3D objects increaseswith the LoD level where LoD0 is the simplest LoD (2.5D; Digital Terrain Model (DTM) + building or roof print) while LoD4 is themost complex LoD (architectural details with interior structures). Semantic information is one of the main components in CityGMLand 3D City Models, and provides important information for any analyses. However, more often than not, the semantic informationis not available for the 3D city model due to the unstandardized modelling process. One of the examples is where a building isnormally generated as one object (without specific feature layers such as Roof, Ground floor, Level 1, Level 2, Block A, Block B,etc). This research attempts to develop a method to improve the semantic data updating process by segmenting the 3D building intosimpler parts which will make it easier for the users to select and update the semantic information. The methodology is implementedfor 3D buildings in LoD2 where the buildings are generated without architectural details but with distinct roof structures. This paperalso introduces hybrid semantic-geometric 3D segmentation method that deals with hierarchical segmentation of a 3D building basedon its semantic value and surface characteristics, fitted by one of the predefined primitives. For future work, the segmentationmethod will be implemented as part of the change detection module that can detect any changes on the 3D buildings, store andretrieve semantic information of the changed structure, automatically updates the 3D models and visualize the results in a userfriendlygraphical user interface (GUI)

IMPROVING SEMANTIC UPDATING METHOD ON 3D CITY MODELS USING HYBRID SEMANTIC-GEOMETRIC 3D SEGMENTATION TECHNIQUE