**Layer of the Deutsche Bahn Scene knowledge**

The layer of object knowledge contains all relevant information about the objects and elements which might be found within a Deutsch Bahn scene. This might comprise a list such as: {Signals, Mast, Schalanlage, etc.}. They are used to fix either the main scene within its point clouds file and size through attributes related to the scene class, or even to characterize detected element with different semantic and geometric characteristics. The created knowledge base related to the Deutsche Bahn scene has been inspired next to our discussion with the domain expert and next to our study based on the official Web site for the German rail way specification "http://stellwerke.de". An overview of the targeted 230 Semantics – Advances in Theories and Mathematical Models

The properties axioms define relationship between classes in the ontology. They are also used to relate an object to other via topological relations. Actually, we found four major object properties axioms in the top level ontology which have their specialized properties for the specialized activities, Fig. 7, DC:hastopologicRelation,

Following to above considerations and with respect to technological possibilities, the current ontology will be modelled in various levels. In principle, we have to distinguish between object-related knowledge and algorithmic related knowledge. We therefore have a layer of the object knowledge and a layer of the algorithmic knowledge containing the respective

The object knowledge layer will be classified in three categories: geometric, topological and semantic knowledge representing a certain scenario (Whiting, 2006) Therefore we

The layer of object knowledge contains all relevant information about the objects and elements which might be found within a Deutsch Bahn scene. This might comprise a list such as: {Signals, Mast, Schalanlage, etc.}. They are used to fix either the main scene within its point clouds file and size through attributes related to the scene class, or even to characterize detected element with different semantic and geometric characteristics. The created knowledge base related to the Deutsche Bahn scene has been inspired next to our discussion with the domain expert and next to our study based on the official Web site for the German rail way specification "http://stellwerke.de". An overview of the targeted

Alg:isDeseignedFor, Geom:hasGeometry, Charac:hasCharacteristics.

**5.2 Properties axioms** 

Fig. 7. Ontology general schema overview.

**5.3 Created knowledge layers** 

**5.3.1 Layers of object knowledge** 

Deutsche Bahn Scene knowledge

**Layer of the Deutsche Bahn Scene knowledge** 

semantic information.

distinguish between:

 Geometric knowledge Topological knowledge elements, the most useful and discriminant characteristics to detect it and their interrelationship is presented in Table 1 .


Table 1. Example of the Deutsche Bahn scene objects

Table 1 shows a possible collection of scene elements in case of a Deutsche Bahn scene. They may be additionally structured in a hierarchical order as might be seen convenient for a scene while Fig8 shows the suggested taxonomical structure to model them within the OWL language.

Basically, a railway signal is one of the most important elements within the Deutsche Bahn scene where we find DC:main\_signals and DC:secondary\_signal. The main signals are classified onto DC:primary\_signal and DC:distant\_signal. In fact, the primary signal is a railway signal indicating whether the subsequent section of track may be driven on. A primary signal is usually announced through a distant signal. The last one indicates which image signal to be expected that will be associated to the main signal in a distance of 1 km. Actually, big variety of secondary signals exists like the DC:Vorsignalbake, the DC:Haltepunkt and others. From the other side, the other discriminant elements within the same scene are the DC:Masts presenting electricity born for the energy alimentation. Usually, masts are distant from 50 m from to others. Finally, the DC:Schaltanlage elements present small electric born connected to the ground. For detection purpose, we define for example a signal as:

From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach 233

Fig. 9. The geometry class hierarchy.

Secondary signal

Table 2. Geometric characteristics overview.

is distant 100m from the distant signal, Fig.11.

**Layer of the topologic knowledge** 

Signals

**Class SubClass Subsub Class Restriction on Line** 

**number** 

Vorsignalbake 1 Vertical line 1 Vertical plane Breakpoint\_table 2 Vertical lines 1 Vertical Plan Chess\_board 1 Vertical line 1 Vertical plane

Distant Signal 1 or 2 Vertical line 0

Basic Signals Main Signal 1 or 2 Vertical line 0

NormalMast Between 5 and 6 2 or 4 vertical lines 0

SchaltSchrank Less than 0,5m 1 vertical plane

Mast BigMast More than 6m 2 or 4 vertical lines 0

Schaltanlage Schalthause Less than 1m 1 Vertical plane

While exploring the railway domain, lots of standard topological rules are imposed; such rules are used to help the driver and to ensure the passengers' security. From our point of view, the created rules are helpful also to verify and to guide the annotation process. In fact, topological knowledge represents adjacency relationships between scene elements. For instance, and in case of the Deutsche Bahn scene, the distance between the distant signal and the main one corresponds to the stopping distance that the trains require. The stopping distance shall be set on specific route and is in the main lines often 1000 m or in a rare case 700 m. Add to that, three to five Vorsignalbake are distant from 75m while then the last one

At semantic view, topological properties describe adjacency relations between classes. For example, the property Topo:isParallelTo allows characterizing two geometric concepts

**Restriction on Planes number** 

1 Horizontal plane
