The<scp>CartACom</scp>model: transforming cartographic features into communicating agents for cartographic generalisation

Duchêne, Cécile; Ruas, Anne; Cambier, Christophe

doi:10.1080/13658816.2011.639302

Cited by 34 publications

(30 citation statements)

References 14 publications

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“…This model builds communication between agents (Duchêne, 2003;Duchêne et al, 2012). Because topographic data in rural areas have no obvious pyramidal organization of the space, it is difficult to identify pertinent disjoint groups of objects.…”

Section: Multi-agent System (Mas) and Generalizationmentioning

confidence: 99%

An ontology-driven multi-agent system for nautical chart generalization

Yan

Guilbert

Saux

2016

Cartography and Geographic Information Science

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On nautical charts, undersea features are portrayed by sets of soundings (depth points) and isobaths (depth contours) from which map readers can interpret undersea features. Different techniques were developed for automatic sounding selection and isobath generalization. These methods are mainly used to generate a new chart from the bathymetric database or from a larger scale chart through selection and simplification. However a part of the process consists in selecting and emphasizing undersea features formed by groups of soundings and isobaths on the chart according to their relevance to maritime navigation. Hence automation of the process requires classification of features and their generalization through the application of a set of operators according not only to geometric constraints but also to their meaning.The objective of this work is to propose a multi-agent system for nautical chart generalization that is driven by the knowledge on the generalization process and the undersea features and their relationships. Firstly, this work provides a featurecentered ontology modeling the generalization process. Then, the MAS structure is introduced where agents access cartographic knowledge stored in the ontology. The MAS makes use of measure algorithms to evaluate constraint violations on the chart in order to decide which generalization operators to apply. The whole model has been implemented to provide generalization plans on a real case study.

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Section: Multi-agent System (Mas) and Generalizationmentioning

confidence: 99%

An ontology-driven multi-agent system for nautical chart generalization

Yan

Guilbert

Saux

2016

Cartography and Geographic Information Science

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“…Many works model the spatial objects by agents such as the works of Duchêne (2004), Regnauld (2001), Ware et al (2003), Jabeur (2006), Sester (2000), Galanda (2003), Sabo (2007), Touya and Duchêne (2011) and Duchêne et al (2012). The strategy presented in Duchêne (2004) offers a good method for automating the generalisation process; nevertheless, it is not flexible enough since it does not permit the agent to choose the best action to perform according to a given situation.…”

Section: State Of Art For On-the-fly Web Map Generalisationmentioning

confidence: 99%

A robust framework for topological relations in the on-the-fly web map generalisation process

Lejdel

Kazar

2017

IJDMMM

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Abstract:With the technological development in the web, great numbers of users want access to spatial data on the web that is specific to their needs. This may be possible by applying the suitable generalisation process which consists to simplify the objects of the map and may transform the topological relations between them into another ones in real-time. This process called on-the-fly generalisation maps. Many approaches were proposed for improving this process, but those do not suffice to guide a powerful and efficient process. In this paper, we will propose a robust framework to transform the topological relationships during the on-the-fly web map generalisation and treat the problem associated to modelling of streets and rivers as linear objects because, in the reality (ground), they have some widths or areas. By considering a road or a river as a line or as an area, topological relationships can be different. We use mainly two types of object; ribbon and regions.

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“…Some solutions have been proposed. Among the different approaches, some are based on multi-agent models [1], [2], [5], [8], [11]. In multi-agent approaches for generalisation, geographical objects are modelled as agents trying to satisfy their own constraints.…”

Section: Figmentioning

confidence: 99%

“…AGENT proves effectiveness in urban zones, where the geographical entities are organised in hierarchies, and for the generalisation of roads, where a road may be subdivided into parts, which may generalise themselves, but need an agent to supervise the operations and maintain continuity between sections. CartACom [3], [5] is a model managing transversal relations between agents. All agents interact in a same level.…”

Section: 1mentioning

confidence: 99%

See 1 more Smart Citation

Representation of Interactions in a Multi-Level Multi-Agent Model for Cartography Constraint Solving

Maudet¹,

Touya²,

Duchêne³

et al. 2014

Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

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International audienceThe objective of cartographic generalisation is to simplify geographic data in order to create legible maps when scale decreases. It often requires to reason at different levels of abstraction (e.g. a building, a city). To automate this process, Multi-Agent approaches have been used since several years, by modelling map objects (e.g. buildings) as autonomous entities trying to solve constraints through appropriate transformations. Yet, those approaches are not able to deal with all situations that appear between cartographic objects in a map. Indeed, though a map intrinsically involves objects that belong to several description, scale or organisation levels, there is no explicit multi-level representation in agent-based cartographic models. Thus we assume that the use of a multi-level multi-agent model would improve the automated generalisation process. Especially, the PADAWAN model is a multi-agent model offering multi-level capabilities which match quite well the requirements for the multi-level organisation of cartographic objects. In this paper, we expose how we use this model on the one hand, to reify multi-level relations between cartographic agents, and on the other hand, to represent the constraints and the actions proposed to solve them, as interactions between the agents

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TheCartACommodel: transforming cartographic features into communicating agents for cartographic generalisation

Cited by 34 publications

References 14 publications

An ontology-driven multi-agent system for nautical chart generalization

An ontology-driven multi-agent system for nautical chart generalization

A robust framework for topological relations in the on-the-fly web map generalisation process

Representation of Interactions in a Multi-Level Multi-Agent Model for Cartography Constraint Solving

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