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Machine learning techniques for determining parameters of cartographic generalisation algorithms / Lagrange (Enseigne de Vaisseau) (2000)  

Public Titre : Machine learning techniques for determining parameters of cartographic generalisation algorithms Type de document : Article/Communication Auteurs : Lagrange (Enseigne de Vaisseau), Auteur ; Landras (Enseigne de Vaisseau), Auteur ; Sébastien Mustière , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2000 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 33-B4 Conférence : ISPRS 2000, 19th ISPRS Congress Technical Commission 4, Mapping and Geographic Information Systems 16/07/2000 23/07/2000 Amsterdam Pays-Bas OA Proceedings archives Importance : pp 718 - 725 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:250.000 [Termes IGN] apprentissage automatique [Termes IGN] BD Carto [Termes IGN] carte routière [Termes IGN] lissage de données [Vedettes matières IGN] Généralisation Résumé : (auteur) This paper reports on research performed in the field of automated map generalization. We address the issue of determining how to set parameters of transformation algorithms. Empirical and theoretical studies have shown that, even given fixed map scale and purpose, these parameter values vary from one object to the other according to different characteristics such as the shape, size or environment of the object. Because of the complexity of cartographic rules and generalisation algorithms, we address this problem with techniques developed in the field of Machine Learning from examples. Specifically, we automatically learn, with neural networks, how to determine an algorithm parameters according to a set of measures describing the object to be transformed. We present the main issues to be addressed to use neural networks. We show that our approach is useful and that its main limit stands in the lack of good measures to describe an object. As a case study, this paper presents results of learning how to set the strength of a smoothing algorithm on a line from the French BDCarto database to represent a road on a 1/250,000 scale road map. Numéro de notice : C2000-029 Affiliation des auteurs : COGIT+Ext (1988-2011) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://www.isprs.org/proceedings/XXXIII/congress/part4/718_XXXIII-part4.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103272