Three dimensional (3D) city model is an interesting research topic in the last decade. This is because achieving
rapid, automatic and accurate extraction of a realistic model for the large urban area is still a challenge. Consequently,
increasing the efficiency of 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 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) algorithm. 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.