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Digital terrain data models in high resolution are required in applications for visualization but also, e.g. for identification of various types of terrain features. These two aspects are in a way contradictory since the former application require a large number of data points to represent the high resolution, while the latter cannot deal with such a large number of data points without high demands for heavy computational powers. A solution to this problem is a structure that includes quantitative characteristics for visualization and a qualitative representation for feature analysis. A digital terrain data model characterized with these dual aspects has been designed and will be presented in this work.

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Driveability analysis is a quite complex problem that for its solution depends on several factors. One of these factors concerns the type of vehicle for which a drive-way should be determined. Besides this, the terrain structure, the type of vegetation but also the ground type and its conditions play important roles. Driveability analysis will consequently include analysis of primarily geographical information and the outcome of this analysis can be used to support decision making in command and control systems. However, quite often the required geographical information is represented in a resolution that is either too low and/or is represented with a high degree of uncertainty that cannot be neglected. In this work, an approach to driveability analysis is presented in which geographical information is regarded as context information that eventually is fused to generate paths, that may be drivable for certain types of vehicles. This information is fused by means of a knowledge-based technique that determines the driveability from a set of qualitative driveability impact factors.