Cartographic generalization of roads in a local and adaptive approach: A knowledge acquistion problem

Mustière, Sébastien

doi:10.1080/13658810509161245

Cited by 38 publications

(16 citation statements)

References 7 publications

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“…Currently, these conditions and measures are understood and used as constraints and map specifications to guide cartographic generalization or to evaluate the final results of the process (Regnauld 2001;Mustiere 2005;Filippovska et al 2008;Stoter et al 2009;Taillandier et al 2011). …”

Section: Cartometric Evaluationmentioning

confidence: 97%

“…Therefore, the automation of cartometric evaluation or part of its evaluation is possible. The evaluation components are known as constraints, restrictions, map specifications, geometric distortions, guidelines, cartographic rules and graphical parameters (Mustiere 2005;Filippovska et al 2008;Stoter et al 2009;Taillandier et al 2011). These aspects can be described as geometric measurements that can be used as expert system rules to perform the automatically cartometric evaluation.…”

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confidence: 99%

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Methodology for Automating the Cartometric Evaluation of Urban Topographic Maps of Brazil

Castro

Sluter

2016

Lecture Notes in Geoinformation and Cartography

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Cartometric evaluation is part of the cartographic generalization process and can indicate which operator should be applied and which features should be generalized. Those decisions are based on the occurrence of geometric conditions and spatial and holistic measures. Those aspects can be understood as legibility and visibility problems, and this knowledge can be described as rules of an expert system. The automation of cartographic generalization is a challenge partly because of subjective decisions made during the process. Cartometric evaluation show this subjectivity. The formalization and automated identification of the representation problems can make this process more holistic and less dependent on human control and influence and, therefore, more efficient. The objective of this paper is to present a method for developing a cartometric evaluation for topographic maps at 1:5000 derived from 1:2000 scale that is appropriate for the Brazilian landscape. Our approach consists of designing an expert system based on a decision tree. We present the first results of the expert system developed using ArcGIS and its application ModelBuilder to detect automatically map representation problems.

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Section: Cartometric Evaluationmentioning

confidence: 97%

mentioning

confidence: 99%

Methodology for Automating the Cartometric Evaluation of Urban Topographic Maps of Brazil

Castro

Sluter

2016

Lecture Notes in Geoinformation and Cartography

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“…Mustiere [42] identifies two major problems associated with reference data: The "[..] potential examples are not available in a suitable form; and they do not represent exact examples of what we intend to do". He solves this problem by creating the training data.…”

Section: Discussion Of the Approach As A Wholementioning

confidence: 99%

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Harig

Burghardt

Hecht

2016

IJGI

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Abstract:Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was developed, tested and evaluated in terms of possible usage in monitoring built-up areas in spatial science at a very fine-grained level. Results show that built-up area boundaries can be delineated automatically with higher quality compared to the settlement boundaries actually used. The approach has been applied to 166 settlement bodies in Germany. The study shows a very efficient way of extracting settlement boundaries from topographic data and maps and contributes to the quantification and monitoring of urban sprawl. Moreover, the findings from this study can potentially guide policy makers and urban planners from other countries.

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“…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). Mustière et al (2000) and Ruas et al (2006) evaluated learning-based methods for the extraction of rules for the generalisation of buildings.…”

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

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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...

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Cartographic generalization of roads in a local and adaptive approach: A knowledge acquistion problem

Cited by 38 publications

References 7 publications

Methodology for Automating the Cartometric Evaluation of Urban Topographic Maps of Brazil

Methodology for Automating the Cartometric Evaluation of Urban Topographic Maps of Brazil

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

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

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