## 5 Results and Outputs  
The project to acquire the base LIDAR data was concluded in 2012 and an entire terrestrial data set was created through the GIS integration of 554 data sheets each of a 1 km \(\times\) 1 km area (Figure 1a). The data was subsequently converted to a dem employing ArcGIS 10.1 with a height map resulting from a thematic analysis (Figure 1b). Further detailed analysis was made as based on the generation of a raster map at a resolution of 25 cm per pixel (Figure 1c), which resulted in a 90 Gb file and a 10 cm resolution resulting in a 263 Gb file. Reviewing the data in 3D through ArcScene, to an extent helped the recognition process through the simple use of monochrome LIDAR height points (Figure 1d); however this process is a heavy one and requires massive processing power.  
Figure 1: (**a**) Terrestrial LIDAR data integration; (**b**) height map based on LIDAR data; (**c**) Valletta 25 cm raster output; (**d**) Dwejra LIDAR Points; (**e**) Filfla rendering; (**f**) Filfla terrestrial and bathymetric data aggregation; (**g**) Mellieha Height-Depth map; (**h**) Valletta LIDAR cross-sectional perspective; (**i**) Pieta VRML output from tin and ortho-imagery; (**j**) 3d-printed Dwejra haptic output.  
Whilst at this stage, most results are still difficult to comprehend to non-experts due to the limited knowledge of the geographical and GIS background behind such outputs, awareness is better served with a 3D rendering using Corel Bryce 7 software (Figure 1e), which renders the spatial points into a physical structure that is instantly recognizable. Interestingly, even when an integrated terrestrial and bathymetric depiction is made (Figure 1f) recognition is still high, mainly due to the unique shape of the small Maltese island of Filfla, which is situated to the southwest of the main island, which island was used as an example. This process resulted in mixed reactions. For example the employment of a homogenous color scheme to the terrestrial and underwater zones created a top-down view of the locality of Mellieha which was unclear to many (Figure 1g). The particular shape in this case misled both experts and novices in that the terrestrial section resembled the walled cities in the Grand Harbor and such was the natural choice of the reviewers due to cognition of known spaces. It is only when the beach to the Northwest of the image was highlighted that recognition was elicited as landmarks became easily identified. This highlighted the need to provide landmark points to help in the identification of positional familiarity. The usefulness of landmarks was verified through the instant recognition, by users, of the Valletta Carmelite Dome depicted in Figure 1h that employed a multi-color cross-sectional analysis of the LIDAR points.  
This process was taken further through the outputs resultant from VRML (Virtual Reality Modelling Language) depiction (Figure 1i), that allows for virtual insertion in known spaces such as the instantly recognizable hospital and marina adjacent to the medieval fortifications. Immersions, apart from helping identification of landmarks, initiate the process of the physical to the virtual environments, even if not allowing manipulation of the data.  
Finally, this phase was concluded with the introduction of a haptic approach resultant from the ERDF project which enabled the 3D printing of the VRML model (Figure 1j that depicts the Gozitan inland sea area of Dwejra).  
Figure 1:Cont.  
The next step describes the process investigated to create middle-state data for eventual conversion of the raster maps into 3D immersive environments, which against the VRML model, allow for interaction and manipulation of the virtual place.