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Grid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 3 (June 2016) . - pp 266 - 282 Titre : Grid pattern recognition in road networks using the C4.5 algorithm Type de document : Article/Communication Auteurs : Jing Tian, Auteur ; Zihan Song, Auteur ; Fei Gao, Auteur ; Feng Zhao, Auteur Année de publication : 2016 Article en page(s) : pp 266 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] exploration de données géographiques [Termes IGN] grille [Termes IGN] reconnaissance de formes [Termes IGN] réseau routier Résumé : (Auteur) Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks. Numéro de notice : A2016-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2015.1062425 En ligne : https://doi.org/10.1080/15230406.2015.1062425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80473 [article]

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