Descripteur
Documents disponibles dans cette catégorie (2536)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data / Kristin Stock in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data Type de document : Article/Communication Auteurs : Kristin Stock, Auteur ; Javid Yousaf, Auteur Année de publication : 2018 Article en page(s) : pp 1087 - 1116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] mesure de similitude
[Termes IGN] ontologie
[Termes IGN] prise en compte du contexte
[Termes IGN] raisonnement spatial
[Termes IGN] toponyme
[Termes IGN] traitement du langage naturelRésumé : (Auteur) Natural language descriptions of location can be complex, involving many different elements and often describing location by reference to other objects. Descriptions may be vague, and their meaning often depends upon the context within which the description has been expressed. Many previous approaches use mathematical models, focus on prepositions, and have had limited success and application. We present an approach to the interpretation of geospatial natural language expressions that uses a knowledge base of expressions for which human interpretations (in the form of degree of match to one of 50 geometric configurations) are known. Our approach interprets new expressions by finding the most similar knowledge base expression and adopting its meaning. We determine expression similarity using four different methods: element match; linguistic collocation approaches (Cosine); wordnet semantic network distance and a new approach that incorporates the contextual aspects of the expression including scale, geometry type, axial structure, image-schema and liquid/solid. As well as preposition, relatum and locatum, we consider spatial adjectives, adverbs, verb and sub-parts of the relatum and locatum. The method that incorporates context was the most successful of the four tested, selecting the same geometric configuration as human respondents in 69% of cases. Numéro de notice : A2018-200 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1432861 Date de publication en ligne : 07/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1432861 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89869
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 1087 - 1116[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)
[article]
Titre : Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Amr Abd-Elrahman, Auteur Année de publication : 2018 Article en page(s) : pp 154 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] drone
[Termes IGN] orthoimage
[Termes IGN] réseau neuronal convolutif
[Termes IGN] zone humideRésumé : (Auteur) Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework. Numéro de notice : A2018-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89550
in ISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 154 - 170[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Do semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)
[article]
Titre : Do semantic parts emerge in convolutional neural networks? Type de document : Article/Communication Auteurs : Abel Gonzalez-Garcia, Auteur ; Davide Modolo, Auteur ; Vittorio Ferrari, Auteur Année de publication : 2018 Article en page(s) : pp 476 - 494 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] reconnaissance d'objets
[Termes IGN] rectangle englobant minimum
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network. Numéro de notice : A2018-408 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1048-0 Date de publication en ligne : 17/10/2017 En ligne : https://doi.org/10.1007/s11263-017-1048-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90882
in International journal of computer vision > vol 126 n° 5 (May 2018) . - pp 476 - 494[article]A formalized 3D geovisualization illustrated to selectivity purpose of virtual 3D city model / Romain Neuville in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)
[article]
Titre : A formalized 3D geovisualization illustrated to selectivity purpose of virtual 3D city model Type de document : Article/Communication Auteurs : Romain Neuville, Auteur ; Jacynthe Pouliot, Auteur ; Florent Poux, Auteur ; Laurent de Rudder, Auteur ; Roland Billen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 3D symbology encoding
[Termes IGN] aide à la décision
[Termes IGN] base de connaissances
[Termes IGN] base de données localisées 3D
[Termes IGN] logiciel de dessin
[Termes IGN] logiciel SIG
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] style cartographique
[Termes IGN] style graphique
[Termes IGN] web mapping
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still underexploited and believe that this is partly due to inefficient visual communication channels across 3D model producers and the end-user. With the development of a formalized 3D geovisualization approach, this paper aims to support and make the visual identification and recognition of specific objects in the 3D models more efficient and useful. The foundation of the proposed solution is a knowledge network of the visualization of 3D geospatial data that gathers and links mapping and rendering techniques. To formalize this knowledge base and make it usable as a decision-making system for the selection of styles, second-order logic is used. It provides a first set of efficient graphic design guidelines, avoiding the creation of graphical conflicts and thus improving visual communication. An interactive tool is implemented and lays the foundation for a suitable solution for assisting the visualization process of 3D geospatial models within CAD and GIS-oriented software. Ultimately, we propose an extension to OGC Symbology Encoding in order to provide suitable graphic design guidelines to web mapping services. Numéro de notice : A2018-341 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7050194 Date de publication en ligne : 18/05/2018 En ligne : https://doi.org/10.3390/ijgi7050194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90555
in ISPRS International journal of geo-information > vol 7 n° 5 (May 2018)[article]A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm / Alireza Chehreghan in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)
[article]
Titre : A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm Type de document : Article/Communication Auteurs : Alireza Chehreghan, Auteur ; Rahim Ali Abbaspour, Auteur Année de publication : 2018 Article en page(s) : pp 255 - 269 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] algorithme génétique
[Termes IGN] analyse de sensibilité
[Termes IGN] appariement de données localisées
[Termes IGN] appariement géométrique
[Termes IGN] données localisées de référence
[Termes IGN] données localisées des bénévoles
[Termes IGN] objet géographique linéaire
[Termes IGN] routeRésumé : (Auteur) Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score. Numéro de notice : A2018-132 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1324823 Date de publication en ligne : 06/06/2017 En ligne : https://doi.org/10.1080/15230406.2017.1324823 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89662
in Cartography and Geographic Information Science > Vol 45 n° 3 (May 2018) . - pp 255 - 269[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkLocal curvature entropy-based 3D terrain representation using a comprehensive Quadtree / Giyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkBinary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkCrowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)PermalinkDésambiguïsation des entités spatiales par apprentissage actif / Amal Chihaoui in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)PermalinkGeneric rule-sets for automated detection of urban tree species from very high-resolution satellite data / Razieh Shojanoori in Geocarto international, vol 33 n° 4 (April 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkVideo event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)PermalinkAn approach to measuring semantic relatedness of geographic terminologies using a thesaurus and lexical database sources / Zugang Chen in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkA comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area / Guodong Du in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)PermalinkEuropean Forest Types: toward an automated classification / Francesca Giannetti in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkGenerating vague neighbourhoods through data mining of passive web data / Paul Brindley in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)PermalinkGenerative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkOpen data, big data : quel renouveau du raisonnement cartographique ? / Emilie Lerond in Cartes & Géomatique, n° 235-236 (mars - juin 2018)PermalinkSimilarity measurement of metadata of geospatial data : an artificial neural network approach / Zugang Chen in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkAnalyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 / François Messner in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkExtraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos / Yu Feng in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)PermalinkFine-grained object recognition and zero-shot learning in remote sensing imagery / Gencer Sumbul in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkLarge-scale remote sensing image retrieval by deep hashing neural networks / Yansheng Li in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkLRAGE : learning latent relationships with adaptive graph embedding for aerial scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)Permalink