Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed - Landmarks: GIScience for Intelligent Services - page 134

Chapter 5
Computational Aspects: How Landmarks
Can Be Observed, Stored, and Analysed

Abstract In this chapter, we will explore how to 'compute' a landmark. We will
look at ways to calculate that some geographic object sticks out from its background.
We will also discuss approaches for selecting the most appropriate landmark for
describing specific spatial situations. Both these aspects are important steps for the
integration of landmarks in computational services. Therefore, in a third part of
this chapter we will discuss commonalities and differences between both aspects,
where and why the presented approaches may fail, and what alternatives there are
for overcoming these shortcomings.

5.1 Computing a Landmark

The previous chapters have established the importance of landmarks for our
understanding of an environment. We have also highlighted how this impacts on our
way of communicating. Furthermore, Chap. 4 has shown that it is possible to capture
at least the principle aspects of landmarks and landmarkness in a formal way,
making them accessible to computers. This chapter will discuss how computers may
be able to populate these formal specifications. This will encompass approaches of
determining whether and how a geographic object sticks out from the background,
as well as how to select the most appropriate landmark in a given situation. Both are
important aspects of integrating landmarks into geospatial services, however, they
are often treated separately.
To provide an initial idea of the differences between both aspects, consider
the situation depicted in Fig. 5.1 . In the situation on the left, some algorithm
determined for each geographic object whether it fulfills the criteria of landmarkness
as discussed in the last chapter. In particular, such an algorithm identifies those
objects that are salient in their local surroundings. The figure in (a) shows all
objects of those contained in the grey areas that the algorithm considered to be
salient. This results in a set of landmark candidates , which form the input for other
algorithms that select the most relevant landmark for a given situation. For example,