A method for building displacement in automated map generalisation

Ruas, Anne

doi:10.1080/136588198241509

Cited by 70 publications

(50 citation statements)

References 1 publication

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“…Other studies have focused mainly on developing new algorithms for specific generalization operators. These algorithms can be roughly categorized into two types: non-contextual operators for an individual building like simplification [24,36], and contextual operators for building groups such as displacement, selection or typification [4][5][6][7][8][9][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

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Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

Wang

Qingsheng

Liu

et al. 2017

IJGI

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Abstract:In map generalization, scale reduction and feature symbolization inevitably generate problems of overlapping objects or map congestion. To solve the legibility problem with respect to the generalization of dispersed rural buildings, selection of buildings is necessary and can be transformed into an optimization problem. In this paper, an improved genetic algorithm for building selection is designed to be able to incorporate cartographic constraints related to the building selection problem. Part of the local constraints for building selection is used to constrain the encoding and genetic operation. To satisfy other local constraints, a preparation phase is necessary before building selection, which includes building enlargement, local displacement, conflict detection, and attribute enrichment. The contextual constraints are used to ascertain a fitness function. The experimental results indicate that the algorithm proposed in this article can obtain good results for building selection whilst preserving the spatial distribution characteristics of buildings.

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Section: Related Workmentioning

confidence: 99%

“…To solve such conflicts, cartographers have conducted a great deal of research [4][5][6][7][8][9][10][11][12]. In these approaches, displacement has been the major generalization operator [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

Wang

Qingsheng

Liu

et al. 2017

IJGI

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“…It is assessed by computing distances between neighbouring buildings and roads, following [44]. This constraint can propose building removal and displacement actions:…”

Section: Settings Of Our Test Casementioning

confidence: 99%

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

Taillandier¹,

Duchêne²,

Drogoul³

2011

Applied Soft Computing

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“…During the displacement process, because road symbols typically have a higher priority than building symbols [1], the overlapping building symbols are often moved. Cartographers move the symbols based on the following cartographic constraint conditions: the symbol should move as little as possible, and its geometrical characteristics should not change [2].…”

Section: Introductionmentioning

confidence: 99%

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

Huang

Qingsheng

Sun³

et al. 2017

IJGI

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Abstract:In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic algorithm (IGA) and improved particle swarm optimization (PSO) to building displacement to solve conflicts. The dual-inheritance framework from the cultural algorithm is adopted in the PSO algorithm to optimize the topologic structure of particles. We generate Pareto optimal displacement solutions using the niche Pareto competition mechanism. The results of experiments comparing IGA and the improved PSO show that the improved PSO outperforms IGA; the improved PSO results in fewer graphic conflicts and smaller movements that better satisfy the movement precision requirements.

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A method for building displacement in automated map generalisation

Cited by 70 publications

References 1 publication

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

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

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

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