Quality Assessment of Cartographic Generalisation

Bard, Sylvain

doi:10.1111/j.1467-9671.2004.00168.x

Cited by 46 publications

(47 citation statements)

References 5 publications

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“…Om te controleren of alle gewenste informatie, labels en relaties aanwezig zijn, kan er gekeken worden naar een selectie van de data die representatief is voor het geheel, waarbij er een vergelijking gemaakt kan worden met de brondata van waaruit gegeneraliseerd is, bijvoorbeeld op de wijze zoals beschreven door Bard (2004). Door het karakteriseren van brondata en gegeneraliseerde data en deze karakteristieken met elkaar te vergelijken op het niveau van objecten, groepen van objecten of op de het niveau van de hele dataset, kan er een kwantitatief en kwalitatief oordeel worden gegeven over de generalisatie.…”

Section: Methodeunclassified

Aanpassing en uitbreiding controlemethodiek BRT : aanbevelingen om de methodiek van externe kwalieteitscontrole voor de BasisRegistratie Topografie aan te passen en uit te breiden

Storm

Davidse²,

Brus

2017

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De attributen 'tdnCode' en 'visualisatieCode' worden minder belangrijk geacht, omdat deze worden afgeleid van de andere attributen.Het attribuut 'namespace' is voor elk object hetzelfde. De controle hiervan is onderdeel van het kwaliteitsaspect 'logische consistentie'. Ook de juiste notatie van 'objectBeginTijd' en 'tijdstipRegistratie' is onderdeel van het kwaliteitsaspect 'logische consistentie'.De controle van de attributen kan, voor zover deze uitvoerbaar zijn, min of meer automatisch. Het eenmalig maken van de scripts kost wat tijd, met name voor het attribuut 'tdnCode', maar kunnen daarna eenvoudig uitgevoerd worden voor elke objectklasse. Dit kost dan verder weinig tijd.De geschatte extra tijd t.o.v. de vorige externe audits is drie dagen.

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Section: Methodeunclassified

Aanpassing en uitbreiding controlemethodiek BRT : aanbevelingen om de methodiek van externe kwalieteitscontrole voor de BasisRegistratie Topografie aan te passen en uit te breiden

Storm

Davidse²,

Brus

2017

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“…With respect to a single building one can identify at least four of these constraints in the literature. The first constraint is intended to prevent strong changes of the building shape and is called concavity constraint (C6, Bard 2004). The second constraint (C7) is called positional accuracy and should prevent that a building's position is altered too much during building displacement.…”

Section: Constraints and Algorithms For Building Generalisationmentioning

confidence: 99%

“…1 to 5) or continuous floatingpoint scores (e.g. 1.0 to 5.0), using different mapping functions (Bard 2004). In our experiment we will use continuous scores in the range of 1.0 = constraint violated to 10.0 = constraint fully satisfied.…”

Section: Evaluating Constraintsmentioning

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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“…[3] proposed three types of assessment functions to determine the quality of cartographic generalization. The first type includes characterization functions, which characterize the geometrical and structural properties of single features or groups of features by means of constraints.…”

Section: Cartographic Constraintsmentioning

confidence: 99%

Automated processing for map generalization using web services

2008

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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.

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Quality Assessment of Cartographic Generalisation

Cited by 46 publications

References 5 publications

Aanpassing en uitbreiding controlemethodiek BRT : aanbevelingen om de methodiek van externe kwalieteitscontrole voor de BasisRegistratie Topografie aan te passen en uit te breiden

Aanpassing en uitbreiding controlemethodiek BRT : aanbevelingen om de methodiek van externe kwalieteitscontrole voor de BasisRegistratie Topografie aan te passen en uit te breiden

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

Automated processing for map generalization using web services

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