Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed - Landmarks: GIScience for Intelligent Services - page 142

Tabl e 5. 4 Attributes of buildings used in the classification of a landmark candidate (from [ 11 ] )

Attribute Description

Building use Public, residential, Outbuilding, . . .

Building label Name or function of building

Length * width in m 2

Size of building

Elongation Ratio length/width

Number of corners Counting corners (normally 4 to 6)

Single building All alone, single in a row, one Neighbor, . . .

Building moved away from road Closest distance to road in m

Building ground area
parcel area

Ratio of building area to parcel area

Number of buildings
100 m 100 m

1
m 2

Density of buildings (direct
neighborhood)

in

Number of Buildings
500 m 500 m

1
m 2

Density of buildings (district) in

Orientation to road Along (length towards road), across (width),

angular, building at corner (in grad)

Orientation to north Angle building length to north in rad

Orientation to neighbor Difference angle to neighbor in rad

Perpendicular angle in building Deviation of angles to normal in rad

Parcel land use Public, residential, commerce and service,

industrial, . . .

Number of buildings on parcel Counting buildings

Special building objects on parcel Number of car ports, winter gardens, . . .

Neighbor land parcel use 0 or 1 (Boolean)

Form of parcel area Number of corners, number of neighbors

of not only identifying salient buildings, but also making attributes contributing to
a building's salience explicit. Thus, it would be easier to generate guidelines for
landmark identification from ID3 than Cobweb.
The approaches discussed so far determine local landmarks, i.e., they allow
identifying geographic objects that stand out from their immediate surroundings.
However, they do not establish relationships between these landmarks and they
do not rank which landmark is the dominating one for a given area, i.e., which
best represents this area in any references made to it. Linking the conceptual ideas
of Raubal and Winter's formal model [ 35 ] with Elias' data mining approach to
determining local landmarks [ 11 ] , Winter et al. [ 55 ] addressed exactly this problem.
Their approach allows for generating a leveled hierarchy of local landmarks.
Algorithm 5.1 illustrates their approach. Fundamentally, it is based on partitioning
an environment using Voronoi diagrams [ 1 ] (Sect. 4.2.5 ) . On the lowest level,
the most salient landmark in each local neighborhood—say an intersection—is
identified using an unsupervised adaptation of the ID3 algorithm (Line 1 ) . These
most salient landmarks form the next higher level in the hierarchy. Step 1 of the
algorithm gets repeated until there is only one landmark left, i.e., the Voronoi
diagram only consists of a single cell.