**2.3 Discussion**

The presented methods for survey modeling and object recognition rely on hand-coded knowledge about the domain. Concepts like "Signals are vertical" and "Signals intersect with the ground" are encoded either explicitly, through sets of rules, or implicitly, through the design of the algorithm. Such hard-coded, rule based approaches tend to be brittle and break down when tested in new and slightly different environments. Additionally, we can deduce that authors model the context but not the 3D processing algorithms, the geometry and the topology. Furthermore, it will be difficult in such a case to extend an algorithm with new rule or to modify the rules to work in new environments. To make it more flexible and efficient, and in contrast with the literature, we opt to use a new data structure labeled ontology. In fact, the last one presents a formal representation of knowledge by a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain where the basic strength of formal ontology is their ability to present knowledge within their taxonomy, relations and conditions, but also to reason in a logical way based on Description Logics DL concepts. Based on these observations, we predict that more standard and flexible representations of facility objects and more sophisticated guidance based algorithms for object detection instead of a standard one, by modeling algorithmical, geometrical and topological knowledge within an ontology structure will open the way to significant improvement in facility modeling capability and generality since it will allow as to create a more dynamic algorithm sequence for object detection based on object's geometries and to make more robust the identification process.
