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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

Urban classification for generalization orchestration / Annabelle Boffet (2000)  

Public Titre : Urban classification for generalization orchestration Type de document : Article/Communication Auteurs : Annabelle Boffet , Auteur ; Caroline Coquerel, 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 132 - 139 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de généralisation [Termes IGN] analyse multiéchelle [Termes IGN] Lamps2 [Termes IGN] milieu urbain [Termes IGN] système d'information géographique [Vedettes matières IGN] Généralisation Résumé : (auteur) Nowadays, many generalization algorithms exist but their management is still problematic. If algorithm design needs to be carried on, especially for contextual generalization, the amelioration of preliminary landscape description is a critical prerequisite. Better descriptions are necessary to improve the understanding of spatial organizations and thus to decide on a better orchestration of the algorithms. To improve automated analysis, we suggest that a given geographical space should no longer be described by its individual objects, but be analysed by several larger entities created by pertinent structuration processes. This paper proposes a method of spatial database interpretation to automatically define decisional entities for the generalization process. It focuses on the creation of urban information from urban topographic data. The first section reviews the requirements of automated generalization, which emphasize the lack of useful information for urban generalization. Our method to automatically derive information on urban structure is then described. Implemented on LAMPS2, an object-oriented GIS, the analysis is based on a goal-directed classification. Lastly, the results for the classification of a 8800-inhabitant-strong town are presented. The paper concludes with on-going research improvements. Numéro de notice : C2000-023 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/part4/132_XXXIII-part4.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102881