This paper presents a novel approach to construct spatially referenced, multidimensional virtual city models from remotely generated point clouds for areas that lack reliable geographical reference data. A multidimensional point cloud is an unstructured array of single, irregular points in a spatial 3D coordinate system plus time stamp. If geospatial reference points are available, a point cloud is geo-referenced. Geo-referenced point clouds contain a high-precision reference dataset. Point clouds can be utilized in a variety of applications. They are particularly suitable for the representation of surfaces, structures, terrain and objects. Point clouds are used here to generate a virtual 3D city model representing the complex, granular cityscape of Jerusalem and its centre, the Old City. The generation of point clouds is based on two data acquisition methods: active data capture by laser scanning and passive data collection by photogrammetric methods. In our case, very high-resolution stereo imagery in visible light and near infrared bands have been systematically acquired an aerial flight campaign. The spatio-temporal data gathered necessitate further processing to extract the geographical reference and semantic features required in a specific resolution and scale. An insight is given into the processing of an unstructured point cloud to extract and classify the 3D urban fabric and reconstruct its objects. Eventually, customised, precise and up-to-date geographical datasets can be made available for a dened region at a dened resolution and scale.