A Constraint-Based Generalization Model Incorporating a Quality Control Mechanism

Author:

Blana Natalia1ORCID,Kavadas Ioannis2,Tsoulos Lysandros1ORCID

Affiliation:

1. Cartography Laboratory, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Zografou, Greece

2. Internal Quality Unit, Hellenic Cadastre, 15562 Holargos, Greece

Abstract

Automation in map production has created the need for modeling the map composition process. Generalization is the most critical process in map composition, with considerable impact on the quality of features portrayed on the maps. Modeling of the generalization process has been an area of research for several years in the international cartographic community. Constraint-based generalization modeling prevailed, and it is evolving to an agent model or to other optimization models. The generalization model presented in this paper is based on constraint-based modeling. It introduces the standardization of the semantic and cartographic generalization process together with an evaluation mechanism for the assessment of the quality of the resulting cartographic data considering simultaneously the preservation of the shape of the portrayed linear and area features. For cartographers, quality management is a key factor in creating an evidence-based, reliable product. To achieve this objective, cartographers, drawing on international experience, should implement a quality policy and adopt a quality management system (QMS) as an integral part of the map production process, starting with the quality assessment of the input data and finishing with the evaluation of the final product.

Publisher

MDPI AG

Subject

General Medicine

Reference41 articles.

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3. Grünreich, D. (1985). Computer-Assisted Generalization, Institut für Angewandte Geodäsie. Papers CERCO Cartography Course.

4. Duchêne, C., Touya, G., Taillandier, P., Gaffuri, J., Ruas, A., and Renard, J. (2018). Multi-Agents Systems for Cartographic Generalization: Feedback from Past and On-Going Research, LaSTIG, équipe COGIT. Available online: https://hal.archives-ouvertes.fr/hal-01682131/document.

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