Intelligent Object Placement and Scaling in Virtual Decision Environments
In complex environments, increasing demand for exploring natural resources by both decision makers and the public is driving the search for sustainable planning initiatives. Among these is the use of virtual environments to support effective communication and informed decision-making. Central to the use of virtual environments is their development at low cost and with high realism.
This paper explores intelligent approaches to objects placement, orientation and scaling in virtual environments such that the process is both accurate and costeffective. The work involves: (1) determining of the key rules to be applied for the classification of vegetation objects and the ways to build an object library according to ecological classes; (2) exploring rules for the placement of vegetation objects based on vegetation behaviours and the growth potential value collected for the research area; (3) developing GIS algorithms for implementation of these rules; and (4) integrating of the GIS algorithms into the existing SIEVE Direct software in such a way that the rules find expression in the virtual environment.
This project is an extension of an integrated research project SIEVE (Spatial Information Exploration and Visualization Environment) that looks at converting 2D GIS data into 3D models which are used for visualization. The aims of my contribution to this research are to develop rules for the classification and intelligent placement of objects, to build a normative object database for rural objects and to output these as 2D billboards or 3D models using the developed intelligent placement algorithms.
Based on Visual Basic Language and ArcObjects tools (ESRI ArcGIS and Game Engine), the outcomes of the intelligent placement process for vegetation objects are shown in the SIEVE environment with 2D images and 3D models. These GIS algorithms were tested in the integrated research project. According to the case study in Victoria, rule-based intelligent placement is based on the idea that certain decisionmaking processes can be codified into rules which, if followed automatically, would yield results similar to those which would occur in the natural environment. Final product produces Virtual Reality (VR) scenes similar to the natural landscapes.
Considering the 2D images and 3D models represented in the SIEVE scenario and the rules (for natural and plantation vegetation) developed in conjunction with scientists in the Victorian Department of Primary Industries (DPI) and other agencies, outcomes will contribute to the development of policies for better land and resource management and link to wide ranging vegetation assessment projects.