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Auteur Nikolaos Sideris |
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Spatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data / Nikolaos Sideris (2019)
Titre : Spatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data Type de document : Thèse/HDR Auteurs : Nikolaos Sideris, Auteur ; Georgios Miaoulis, Directeur de thèse ; Djamchid Ghazanfarpour, Directeur de thèse Editeur : Limoges : Université de Limoges Année de publication : 2019 Importance : 136 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Limoges spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] aide à la décision
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multisources
[Termes IGN] géoréférencement
[Termes IGN] géovisualisation
[Termes IGN] image 3D
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modélisation sémantique
[Termes IGN] ontologie
[Termes IGN] planification urbaine
[Termes IGN] système d'information urbain
[Termes IGN] urbanismeIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this thesis we propose an approach to address the preceding challenges availed with machine learning techniques with the random forests classifier as its dominant method in a system that combines, blends and merges various types of data from different sources, encode them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier. The data are also forwarded to alternative classifiers and the results are appraised to confirm the prevalence of the proposed method. The data retrieved stem from a multitude of sources, e.g. open data providers and public organizations dealing with urban planning. Upon their retrieval and inspection at various levels (e.g. import, conversion, geospatial) they are appropriately converted to comply with the rules of the semantic model and the technical specifications of the corresponding subsystems. Geometrical and geographical calculations are performed and semantic information is extracted. Finally, the information from individual earlier stages along with the results from the machine learning techniques and the multicriteria methods are integrated into the system and visualized in a front-end web based environment able to execute and visualize spatial queries, allow the management of three-dimensional georeferenced objects, their retrieval, transformation and visualization, as a decision support system. Note de contenu : Introduction
1- Theorical background and State of the Art
2- Thesis contribution to semantic querying, navigation and spatial decision Making of 3D Urban Scenes using Machine Learning
3- Evaluation discussion et conclusionsNuméro de notice : 25995 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Université de Limoges : 2019 Organisme de stage : XLIM (Limoges) nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02449667/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96808