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A hidden Markov model for matching spatial networks / Benoit Costes in Journal of Spatial Information Science, JoSIS, n° 18 (2019)  

Public [article]  inJournal of Spatial Information Science, JoSIS > n° 18 (2019) . - pp 57 - 89 Titre : A hidden Markov model for matching spatial networks Type de document : Article/Communication Auteurs : Benoit Costes , Auteur ; Julien Perret , Auteur Année de publication : 2019 Projets : SODUCO / Perret, Julien Article en page(s) : pp 57 - 89 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de sensibilité [Termes IGN] appariement de réseaux [Termes IGN] modèle de Markov caché [Termes IGN] modèle topologique réseau [Termes IGN] réseau ferroviaire [Termes IGN] réseau fluvial [Termes IGN] réseau routier Résumé : (auteur) Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm is robust in regards to data heterogeneities and imperfections (geometric discrepancies and differences in level of details) and adaptable to match any type of spatial networks. It also has the advantage of requiring no mandatory parameters, as proven by a sensitivity exploration, except a distance threshold that filters potential matching candidates in order to speed-up the process. Finally, a comparison with a commonly cited approach highlights good matching accuracy and completeness. Numéro de notice : A2019-274 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2019.18.489 Date de publication en ligne : 01/07/2019 En ligne : https://doi.org/10.5311/JOSIS.2019.18.489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95337 [article]