Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed - Landmarks: GIScience for Intelligent Services - page 149

Fig. 5.8 ( a ) Turning right at an intersection with three buildings as landmark candidates (only
relevant façades are considered); ( b ) the resulting overall salience values. Adapted from [ 26 ]
configuration of the street network and the route along this network. This results in
a distribution of salience values for different landmark candidates, which allows for
a selection of the most suited, i.e., most relevant landmark for a specific wayfinding
situation. Usually, landmarks located at an intersection are structurally more salient
than those between intersections along a street segment, and those passed before a
turning action are more salient than those after a turn. Figure 5.8 illustrates a street
intersection and the different salience values of the buildings located there. You can
clearly observe the emergence of a salience distribution, with the façade facing the
ingoing street segment of the building located before the intersection being the most
salient one in this case.
This is an important insight. In wayfinding, relevance of a landmark depends on
its location relative to the street network. Consequently, it is important to being
able to compute this location. In principle, this is a geometric problem, which
could be solved by calculating various distances and angles for all possible different
configurations. Such geometric solutions are complicated by the fact that landmarks
may not only be represented as points, but may also be linear or area-like [ 17 ] .
This will have an influence on the geometric operations required to determine their
relative location with respect to the street network.
Richter [ 36 , 40 ] presented an alternative approach by using qualitative descrip-
tions of a landmark's location. These descriptions reflect how landmarks are referred
to in human descriptions (see Chap. 6 ) , employing concepts, such as 'before' or
'after' a turn. In a nutshell, this approach exploits (circular) ordering information
to determine a landmark's relative location, which works for different landmark
geometries.
This approach—as many others—models a street network as a graph-like repre-
sentation, where nodes represent intersections and edges represent street segments.