Centre dedocumentation
scientifique

vol 21 n° 3 - July - September 2017 - Special issue [included] on Map interaction (Bulletin de Geoinformatica) / Christian Kray  

Public [n° ou bulletin]  est un bulletin de Geomatica / Canadian institute of geomatics = Association canadienne des sciences géomatiques (Canada) (1993 -)   Titre : vol 21 n° 3 - July - September 2017 - Special issue [included] on Map interaction Type de document : Périodique Auteurs : Christian Kray, Éditeur scientifique ; Falko Schmid, Éditeur scientifique ; Holger Fritze, Éditeur scientifique Année de publication : 2017 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] carte d'intérieur [Termes IGN] cartographie dynamique [Termes IGN] cartographie pour écran mobile [Termes IGN] données localisées des bénévoles [Termes IGN] langage de requête [Termes IGN] navigation [Termes IGN] requête (informatique) Numéro de notice : sans Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique En ligne : https://link.springer.com/journal/10707/21/3/page/1 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=28705 [n° ou bulletin]

Contient

• Index-supported pattern matching on tuples of time-dependent values / Fabio Valdés in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• Popularity-aware collective keyword queries in road networks / Sen Zhao in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• Humaine : a ubiquitous smartphone-based user heading estimation for mobile computing systems / Nesma Mohssen in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• Efficient maximal reverse skyline query processing / Farnoush Banaei-Kashani in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• PerSE : visual analytics for calendar related spatiotemporal periodicity detection and analysis / Brian Swedberg in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• How users perceive transparency in the 3D visualization of cadastre : testing its usability in an online questionnaire / Chen Wang in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• Controllability matters : The user experience of adaptive maps / Peter Kiefer in Geoinformatica, vol 21 n° 3 (July - September 2017)  
• Interactive shearing for terrain visualization : an expert study / Jonas Buddeberg in Geoinformatica, vol 21 n° 3 (July - September 2017)  

Rule-guided human classification of Volunteered Geographic Information / Ahmed Loai Ali in ISPRS Journal of photogrammetry and remote sensing, vol 127 (May 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 127 (May 2017) . - pp 3 – 15 Titre : Rule-guided human classification of Volunteered Geographic Information Type de document : Article/Communication Auteurs : Ahmed Loai Ali, Auteur ; Zoe Falomir, Auteur ; Falko Schmid, Auteur ; Christian Freksa, Auteur Année de publication : 2017 Article en page(s) : pp 3 – 15 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classification [Termes IGN] données descriptives [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] exploration de données [Termes IGN] imprécision des données [Termes IGN] production participative [Termes IGN] règle d'association [Termes IGN] relation topologique Résumé : (auteur) During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants’ local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass-related features like forest, garden, park, and meadow. The findings of this study indicate the feasibility of the proposed approach. Numéro de notice : A2017-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85093 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017051	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2017053	DEP-EXM	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2017052	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt