Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence

Weibel, Robert; Keller, S.; Reichenbacher, Tumasch

doi:10.1007/3-540-60392-1_10

Cited by 49 publications

(39 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem of the generalisation automation is complex. One approach to solve it is to use a local, step-by-step and knowledge-based method [32][33][34]: each vector object of the database (representing a building, a road segment, etc.) is modified by application of a sequence of generalisation algorithms realising atomic transformations.…”

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

“…Nowadays, this knowledge adaptation is done "manually" by generalisation experts and is often long and fastidious. Indeed, it requires facing the problem of knowledge collecting and formalising [33]. Thus, it is interesting to give the system capabilities to revise by itself its own knowledge base.…”

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

“…Thus, it is interesting to give the system capabilities to revise by itself its own knowledge base. Several works have already used Machine Learning to learn relevant control knowledge [33,35,36] but only few have proposed to automatically revise existing knowledge. Among them, Burghardt and Neun [37] proposes to use previously generalised objects to build a case base.…”

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

See 2 more Smart Citations

Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

Taillandier¹,

Duchêne²,

Drogoul³

2011

Applied Soft Computing

View full text Add to dashboard Cite

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

Section: Automatic Cartographic Generalisationmentioning

confidence: 99%

See 1 more Smart Citation

Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

Taillandier¹,

Duchêne²,

Drogoul³

2011

Applied Soft Computing

View full text Add to dashboard Cite

“…Hence, research turned to knowledge acquisition during the subsequent years, including the utilisation of machine learning techniques for the extraction of generalisation processing rules. After initial experiments by Weibel et al (1995), Plazanet et al (1998) developed a supervised learning approach for the selection of appropriate line generalisation algorithms, primarily for roads. This work was later extended by Mustière (2005).…”

Section: Related Work To Improve the Performance Of Generalisation Symentioning

confidence: 99%

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

Steiniger

Taillandier

Weibel

2010

International Journal of Geographical Information Science

Self Cite

View full text Add to dashboard Cite

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 1:25 000. 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. 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 to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are t...

show abstract

“…Automated generalization can be seen as an iterative process between conflict analysis and conflict resolution [30], [41]. Thus, the goal of generalization is to minimize the existing Fig.…”

Section: Cost Functionmentioning

confidence: 99%

Automated processing for map generalization using web services

2008

Self Cite

View full text Add to dashboard Cite

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequences.

show abstract

Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence

Cited by 49 publications

References 7 publications

Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

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

Automated processing for map generalization using web services

Contact Info

Product

Resources

About