International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
It is because the algorithm constitutes the rule stating it is 
designed for data having height as shown in equation 3. 
HAA 3 isDesignedFor.Data3D T1 Data3D.hasHeight.(»0) (3) 
The execution of the algorithm detects prominent geometries of 
dominant and simple objects in the scene. Qualification follows 
detections. This is carried out through extended SWRL. 
Examples can be seen in equation 1 and 2. 
As stated, three possible qualifications are possible: 
unambiguous, ambiguous and unknown. For simplicity we 
carry forward this discussion with the first case where the 
objects are qualified distinctly. The WiDOP platform utilizes 
the semantic rules (defined through property restrictions) to 
infer the algorithm or algorithmic sequence for verification. We 
illustrate this with the two prominent objects in the DB scene: 
Mast (poles carrying cables for powering trains) and Signal. 
Figure 9 presents point cloud subsets for them. 
The classes of types DomainConcept is related to that of 
Geometry through specialized properties of hasGeometry (see 
fig 2). 
DCE 3 hasGeometry.Geometry (4) 
   
(a) (b) 
Figure 9. Point cloud sets (a) Signal (b) Mast 
The specializations of class Geometry are possible geometry 
types (fig. 5a). For instance Line3D is a type of _3D which is a 
specialization of the class Geometry. 
Geometry E_3DE Line_3D (5) 
Putting together equation 4 and 5, we can conclude that Mast 
has Line 3D. Furthermore, we can also say Mast has Line 3D 
with dense, linearly arranged points (we term them as thick 
lines for simplicity) as shown in equation 6, and Signal has 
Line 3D with a low density of points (called thin lines for 
simplicity), as shown in equation 7. Here thick and thin are 
characteristics of the line. These characteristics (termed 
hasChar in DL equations) are helpful in determining the input 
parameters for the algorithms. It will be discussed later but for 
now we present how ASM uses these simple rules in 
algorithmic selection. 
Mast = 3 hasLine3D.Line3D 1 Line3D.hasChar.{ Thick} (6) 
Signal = 3 hasLine3D.Line3D MN Line3D.hasChar.{ Thin} (7) 
94 
The reasoning engine of the underlying ASM infers the rules 
against the rules within sub-classes of Algorithms. Algorithm 
LineDetectionin3DbyRANSAC is recommended, considering 
that it is designed for 3D lines. 
LineDetectionin3DbyRANSAC = 3 isDesignedFor.Line3D (8) 
The reasoning engine inside ASM implements the same 
principle to infer algorithms for other knowledge domains. We 
have implemented it against the data knowledge under class 
Data. Presuming that the standard deviation of a dataset 
establishes a noise value for that dataset, ASM then infers the 
algorithms best suited for datasets containing noise. It shows 
the use of universal knowledge through combining different 
knowledge domains (related to the scene, to classes of objects, 
to instruments and so on), allowing the ASM to interact with 
them. This interaction helps in providing answers for detecting 
objects in extreme situations. However, it is necessary to define 
appropriate rules to determine the usage of these knowledge 
domains. Likewise, the underlying knowledge schema (fig. 2) 
provides freedom in choosing its data source. We use a 3D 
point cloud from the DB scene for our case, however it is also 
possible to use images or other data formats. 
3.2. Simulation Knowledge 
Algorithms behave differently in different situations, for 
instance reflecting differences in geometry or data. Even two 
characteristics of the same geometry might need to be 
addressed in the detection algorithm. As shown in Equation 6 
and 7, the ASM chooses LineDetection3DbyRANSAC for 
detecting the geometries, but using the same parameters for 
both cases might not yield best results. In principle, it should 
use different parameters for different point densities of the 
linear structure to capture most of the points within the linear 
structure (fig. 10). We thus need a higher radius value for thick 
lines and a lower radius value for the thin. 
Dist thre: =0.1 
Figure 10. Cylinder radius for detection 
This is exactly the intention behind obtaining and modeling the 
simulation knowledge into the KB. Each observation of the 
execution of an individual algorithm is induced in the KB. 
These simulations are based on the results as they are tested 
against different data, geometries, and other characteristics. The 
clear benefit is that in above given situations for Mast and 
Signal, the ASM of the WiDOP platform selects a different 
radius threshold for the LineDetectionin3DbyRANSAC 
algorithm. Furthermore, the ASM can evaluate the rules defined 
by the scene to select different algorithms for different cases. 
Instead of LineDetectionin3DbyRANSAC, ASM recommends 
2DHoughTransformation for example, if the detection process 
uses images or any 2D data as source data. 
3.3. The Result 
Our approach was tested with a 500 m long 3D point cloud of 
the Nuremberg main station (Niirnberg Hbf). The KB consists 
of tl 
coul 
Tab 
scer 
105 
ann 
curr 
sets 
afte 
Tab 
moc 
sho 
The 
is S 
moc 
in tl 
The 
suit 
pre 
bee 
the 
bas 
that 
suit 
pro 
inte 
kno 
add 
Thi 
rese 
edu 
the 
Chr 
Ref 
Ang 
A, 
segr 
Patt 
Die; 
Bec 
Lan 
ref/