Utilising urban context recognition and machine learning to improve the generalisation of buildings

  • Steiniger S
  • Taillandier P
  • Weibel R
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Abstract

The introduction of automated generalisation procedures in map
production systems requires that generalisation systems are capable of
processing large amounts of map data in acceptable time and that
cartographic quality is similar to traditional map products. With
respect to these requirements, we examine two complementary approaches
that should improve generalisation systems currently in use by national
topographic mapping agencies. Our focus is particularly on
self-evaluating systems, taking as an example those systems that build
on the multi-agent paradigm. The first approach aims to improve the
cartographic quality by utilising cartographic expert knowledge relating
to spatial context. More specifically, we introduce expert rules for the
selection of generalisation operations based on a classification of
buildings into five urban structure types, including inner city, urban,
suburban, rural, and industrial and commercial areas. The second
approach aims to utilise machine learning techniques to extract
heuristics that allow us to reduce the search space and hence the time
in which a good cartographical solution is reached. Both approaches are
tested individually and in combination for the generalisation of
buildings from map scale 1:5000 to the target map scale of 1:25000. Our
experiments show improvements in terms of efficiency and effectiveness.
We provide evidence that both approaches complement each other and that
a combination of expert and machine learnt rules give better results
than the individual approaches. Both approaches are sufficiently general
to be applicable to other forms of self-evaluating, constraint-based
systems than multi-agent systems, and to other feature classes than
buildings. Problems have been identified resulting from difficulties to
formalise cartographic quality by means of constraints for the control
of the generalisation process.

Author-supplied keywords

  • Building generalisation
  • Data enrichment
  • Expert knowledge
  • Machine learning
  • Map generalisation
  • Multi-agent systems
  • Self-evaluating systems

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