Transactions in GIS . vol 20 n° 3Paru le : 01/06/2016 |
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Ajouter le résultat dans votre panierAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)
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Titre : An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) Type de document : Article/Communication Auteurs : Kambiz Borna, Auteur ; Antoni B. Moore, Auteur ; Pascal Sirguey, Auteur Année de publication : 2016 Article en page(s) : pp 368–381 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] données vectorielles
[Termes IGN] image Ikonos
[Termes IGN] modèle orienté agentRésumé : (auteur) Spatial modeling methods usually use pixels and image objects as fundamental processing units to address real-world objects, geo-objects, in image space. To do this, both pixel-based and object-based approaches typically employ a linear two-staged workflow of segmentation and classification. Pixel-based methods segment a classified image to address geo-objects in image space. In contrast, object-based approaches classify a segmented image to identify geo-objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo-objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real-world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object-Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification. Numéro de notice : A2016-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12226 En ligne : http://dx.doi.org/10.1111/tgis.12226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81390
in Transactions in GIS > vol 20 n° 3 (June 2016) . - pp 368–381[article]Spatial discovery and the research library / Sara Lafia in Transactions in GIS, vol 20 n° 3 (June 2016)
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Titre : Spatial discovery and the research library Type de document : Article/Communication Auteurs : Sara Lafia, Auteur ; Jon Jablonski, Auteur ; Werner Kuhn, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 399 – 412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] accès aux données localisées
[Termes IGN] bibliothèque numérique
[Termes IGN] découverte de connaissances
[Termes IGN] données ouvertes
[Termes IGN] métadonnées géographiques
[Termes IGN] plateforme logicielle
[Termes IGN] publication
[Termes IGN] recherche scientifique
[Termes IGN] système d'information géographique
[Termes IGN] web des donnéesRésumé : (auteur) Academic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources. The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri's Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries. Numéro de notice : A2016-461 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12235 En ligne : http://dx.doi.org/10.1111/tgis.12235 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81391
in Transactions in GIS > vol 20 n° 3 (June 2016) . - pp 399 – 412[article]