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Auteur Georgios Miaoulis |
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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 Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes / Christos Yiakoumettis in Geoinformatica, vol 18 n° 1 (January 2014)
[article]
Titre : Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes Type de document : Article/Communication Auteurs : Christos Yiakoumettis, Auteur ; Nikolaos Doulamis, Auteur ; Georgios Miaoulis, Auteur ; Djamchid Ghazanfarpour, Auteur Année de publication : 2014 Article en page(s) : pp 27 - 62 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage dirigé
[Termes IGN] Athènes
[Termes IGN] exploration de données
[Termes IGN] itinéraire
[Termes IGN] métadonnées
[Termes IGN] navigation
[Termes IGN] ontologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] personnalisation
[Termes IGN] pondération
[Termes IGN] réalité virtuelle
[Termes IGN] système d'information géographique
[Termes IGN] utilisateurRésumé : (Auteur) The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens. Numéro de notice : A2014-027 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10707-013-0176-0 Date de publication en ligne : 12/04/2013 En ligne : https://doi.org/10.1007/s10707-013-0176-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32932
in Geoinformatica > vol 18 n° 1 (January 2014) . - pp 27 - 62[article]Réservation
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