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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]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017051	RAB	Revue	Centre de documentation	En réserve L003	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

A review of volunteered geographic information quality assessment methods / Hansi Senaratne in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 1-2 (January - February 2017) . - pp 139 - 167 Titre : A review of volunteered geographic information quality assessment methods Type de document : Article/Communication Auteurs : Hansi Senaratne, Auteur ; Amin Mobasheri, Auteur ; Ahmed Loai Ali, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 139 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] acquisition de données [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de données géographiques [Termes IGN] qualité des données Résumé : (auteur) With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI. Numéro de notice : A2017-030 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1189556 En ligne : http://dx.doi.org/10.1080/13658816.2016.1189556 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84023 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017012	RAB	Revue	Centre de documentation	En réserve L003	Disponible