GIS – 3D and Beyond



3. TWO AND A HALF-DIMENSIONAL (2.5D) GIS
This is an extension of the system discussed in the preceding section that is by adding height to the existing datasets such as height data from contours, heights from photogrammetric workstation or any other acquisition techniques like GPS or Lidar data acquisition system. At the moment some GIS software offers surface data manipulation module as part of the typical GIS modules or programs. The module is just to manipulate heights and do some surface analysis like contouring, slope and aspect and other computations on top of the typical 2D data layers. The author considers 2.5D GIS is the starting point that one should consider in developing such 3D system. The modeling component is very interesting to look at. Many research efforts have been done in this spatial data modeling domain such as Molenaar (1991), Pilouk (1996), Zlatanova, and Peng (1997). Molenaar proposed a Formal Data Structure (FDS) to link between various primitives as illustrated in Figure 1. The basic relationships of object primitives is quite clear and could be utilized to certain extent but not quite suitable for GIS data with heights as we have in digital terrain model (DTM). The model then has been modified by Pilouk (1996) for a spatial system as we called it a 2.5D GIS. As a result, several GIS software or systems are based on this model. Manipulation of terrain data together with other features is now possible by using the 2.5D data model. Query like “show a group of polygon features on the terrain” could be performed quite nicely (see Figure 2).




Figure 2. Queries from 2.5D data model (Courtesy of Pilouk)


4. THREE-DIMENSIONAL (3D) GIS
It is interesting to note that the Molenaar’s data model could be extended for 3D GIS software development. Several researchers like Zlatanova (2000) and Abdul-Rahman (2000) have utilized the model for manipulating 3D spatial objects in their works. The adapted model as illustrated in Figure 3 shows the relationships of 3D object’s primitives like nodes, lines, surfaces, and solids. The model works for both data in raster as well as for vector datasets.


Figure 3. The 3DFDS model


Abdul-Rahman (2000) manipulated 3D objects via 3D raster-based (voxel) objects approach and had generated information via proprietary object-oriented DBMS (i.e., POET DBMS –Persistent Object and Extended Technology). The results show the 3DFDS is capable of manipulating 3D spatial objects as the objects were created via 3D triangular irregular network approach.


Figure 4. The 3DTIN and 3D raster polygons (in slices)


Zlatanova (2000) implemented the modified version of 3DFDS for Web-based 3DGIS. The model works in Oracle spatial DBMS as the 3D objects were constructed in VRML coding environment, see Figure 5.


Figure 5. The Web based query on the generated 3D objects (Courtesy of Zlatanova)


The development of 3D GIS is growing and many works are being done in several research centers and universities as indicated by Abdul-Rahman (2006).

5. BEYOND 3D GIS
The author believes that one day GIS will have a true 3DGIS in near future. This is based on the current pace of research efforts that being done in various centers and universities in some parts of the globe. Current trends clearly show that GIS users demand more than the current technology could offer. Here, future users would like to have information of a particular object in a certain regions or areas in a split of seconds, very accurate, and easy to access either standalone or Web/Internet solution (Hunter and Tao, 2002). Although theoretically, the spatial modeling of objects could be extended to multi-dimensional (nD), the computing visualization systems only permit up to 3D environment. This section attempts to highlight some possible research works that could lead to the “future” GIS or ubiquitous GIS. The author believe that this is the future trend of GIS where every component in GIS like data collection, data manipulation, databasing, and reporting (and visualization) were done seamlessly and they are highly dependent on mobile computing environment. We have seen several research groups are working on this direction e.g. GeoICT Lab, York University, Toronto, Canada; GIS Section at TU Delft, The Netherlands, and Fraunhofer Institute (IGD), Darmstadt, Germany. This newly established research initiative in GIS focuses on components integration and mobile, thus the system could end up in small size with some intelligence-built in components. The following figure shows basic configuration for the ubiquitous geospatial system. In general, voice sensor, small display, small power unit, precise location finder (like GPS or Galileo), wireless communication system, and equipped with compact size computer could be parts of the whole system.


Figure 6. The ubiquitous GIS system (Courtesy of Tao)


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