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DIOGEN, a multi-level oriented model for cartographic generalization

Adrien Maudet 1, 2, 3 Guillaume Touya 3 Cécile Duchêne 3 Sébastien Picault 1, 2, 4
1 SMAC - Systèmes Multi-Agents et Comportements
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 COGIT - Cartographie et Géomatique
LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique
Abstract : Among approaches for automated generalization of vector data, we focus on the multi-agent paradigm: cartographic objects are modeled as agents (autonomous objects) that apply generalization algorithms to themselves to satisfy cartographic constraints. Several agent levels are considered, for example, individual agents, such as a building, and agents representing a group of agents, such as an urban block composed of the surrounding roads and contained buildings. Several multi-agent models were proposed to automate the orchestration of map generalization processes. Existing multi-agent generalization models have different approaches to manage the relations between agent levels. In this paper, we unify existing models, adapting a multi-level simulation model, to simplify interactions between agents in different levels. We propose the DIOGEN model, in which the principle of interactions between agents of different levels is adapted to constraint-driven cartographic generalization. DIOGEN unifies three existing multi-agent generalization models (AGENT, CartACom and GAEL), combine their behaviors and take advantage of their skills. Our proposal is evaluated on different use cases: instances of topographic mapping, and mapping of hiking routes over topographic data as an example of thematic mapping.
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Contributor : Cristal Equipe Smac <>
Submitted on : Monday, June 12, 2017 - 9:19:30 AM
Last modification on : Thursday, May 6, 2021 - 3:35:10 AM



Adrien Maudet, Guillaume Touya, Cécile Duchêne, Sébastien Picault. DIOGEN, a multi-level oriented model for cartographic generalization. International Journal of Cartography, Taylor & Francis, 2017, 3 (1), pp.121-133. ⟨10.1080/23729333.2017.1300997⟩. ⟨hal-01536627⟩



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