Three dimensions (3D) city model is an interesting research topic in the last decade. This is because
achieving the rapid, automatic and accurate extraction of a realistic model for the large urban area is still a challenge.
Consequently, increasing the efficiency of the 3D city modeling is required. The objective of this research is to develop
a simple and efficient semi-automatic approach to generate a 3D city model for the urban area using the fusion of
LiDAR data and Ortho-rectified imagery. These data sources provide efficiency for 3D building extraction. This
approach uses both LiDAR and imagery data to delineate building outlines, based on fuzzy c-means clustering (FCM).
The third dimension is obtained automatically from the normalized digital surface model (nDSM) using spatial analyst
tool. The 3D model is then generated using the multi-Faceted patch. The accuracy assessment for both height and
building outlines is conducted referring to the ground truth, and by means of visual inspection and different quantitative
statistics. The results showed that the proposed approach can successfully detect different types of buildings from
simple rectangle to circular shape and LOD2 (level of detail) is formed by including the roof structures in the model.