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Spatiotemporal data model for network time geographic analysis in the era of big data / Bi Yu Chen in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 1041 - 1071 Titre : Spatiotemporal data model for network time geographic analysis in the era of big data Type de document : Article/Communication Auteurs : Bi Yu Chen, Auteur ; H. Yuan, Auteur ; Qingquan Li, Auteur Année de publication : 2016 Article en page(s) : pp 1041 - 1071 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] entité géographique [Termes IGN] milieu urbain [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] temps Résumé : (Auteur) There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities. Numéro de notice : A2016-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1104317 En ligne : https://doi.org/10.1080/13658816.2015.1104317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80878 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)  

Public [article]  inGeocarto international > vol 26 n° 6 (October 2011) . - pp 435 - 457 Titre : Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data Type de document : Article/Communication Auteurs : S. Khorram, Auteur ; H. Yuan, Auteur ; F. Van Der Wiele, Auteur Année de publication : 2011 Article en page(s) : pp 435 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multirésolution [Termes IGN] carte de Kohonen [Termes IGN] classification par réseau neuronal [Termes IGN] données multicapteurs [Termes IGN] fusion d'images [Termes IGN] image Landsat-TM [Termes IGN] image SPOT [Termes IGN] occupation du sol [Termes IGN] Perceptron multicouche [Termes IGN] précision de la classification Résumé : (Auteur) Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat Thematic Mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult. Numéro de notice : A2011-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.600462 Date de publication en ligne : 10/08/2011 En ligne : https://doi.org/10.1080/10106049.2011.600462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31181 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2011061	RAB	Revue	Centre de documentation	En réserve L003	Disponible