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Spatial collective intelligence? Credibility, accuracy, and volunteered geographic information / Seth Spielman in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 2 (March 2014) . - pp 115 - 124 Titre : Spatial collective intelligence? Credibility, accuracy, and volunteered geographic information Type de document : Article/Communication Auteurs : Seth Spielman, Auteur Année de publication : 2014 Article en page(s) : pp 115 - 124 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées des bénévoles [Termes IGN] intelligence collective [Termes IGN] interactivité [Termes IGN] participation du public [Termes IGN] production participative [Termes IGN] qualité de service [Termes IGN] qualité des données Résumé : (Auteur) Collective intelligence is the idea that under the right circumstances collections of individuals are smarter than even the smartest individuals in the group, that is a group has an “intelligence” that is independent of the intelligence of its members. The ideology of collective intelligence undergirds much of the enthusiasm about the use of “volunteered” or crowd-sourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this article identifies four pre-conditions for the emergence of collective intelligence and then examines the extent to which collectively generated mapping systems satisfy these conditions. However, the “intelligence” collectively generated maps is hard to assess because there are two difficult to reconcile perspectives on map quality – the credibility perspective and the accuracy perspective. Much of the current literature on user-generated maps focuses on assessing the quality of individual contributions. However, because user-generated maps are complex social systems and because the quality of a contribution is difficult to assess this strategy may not yield an “intelligent” end product. The existing literature on collective intelligence suggests that the structure of groups is more important than the intelligence of group members. Applying this idea to user-generated maps suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The article concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs. Numéro de notice : A2014-206 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1080/15230406.2014.890546 En ligne : https://doi.org/10.1080/15230406.2013.874200 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33109 [article]

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032-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A study-based ranking of LiDAR data visualization schemes aided by georectified aerial images / Suddasheel Ghosh in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 2 (March 2014) . - pp 138 - 150 Titre : A study-based ranking of LiDAR data visualization schemes aided by georectified aerial images Type de document : Article/Communication Auteurs : Suddasheel Ghosh, Auteur ; Bharat Lohani, Auteur ; Neeraj Misra, Auteur Année de publication : 2014 Article en page(s) : pp 138 - 150 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] orthoimage [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network [Termes IGN] visualisation de données Résumé : (Auteur) Light Detection and Ranging (LiDAR) collects dense 3D topographic information in the form of points. LiDAR data can be displayed either through direct rendering of the point cloud or by generalizing features extracted through classification or segmentation. We are working in the domain of visualizing LiDAR data sets and have developed certain pipelines for visualization. These pipelines have been presented elsewhere. We present a technique for the evaluation of visualization schemes for LiDAR data, by conducting a visualization experience survey for 13 pre-processing and visualization schemes where 60 participants rated these schemes on a 10 point scale on a questionnaire. The paper establishes a ranking for the different visualization schemes described herein. Finally, this paper establishes that our heuristic-based algorithm (presented elsewhere) performs almost equal to a classification-based visualization pipeline made using professional software. We believe that the presented technique can be used to assess other geospatial visualization schemes. Numéro de notice : A2014-207 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15230406.2014.880071 En ligne : https://doi.org/10.1080/15230406.2014.880071 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33110 [article]

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Model generalization of two different drainage patterns by self-organizing maps / Alper Sen in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 2 (March 2014) . - pp 151 - 165 Titre : Model generalization of two different drainage patterns by self-organizing maps Type de document : Article/Communication Auteurs : Alper Sen, Auteur ; Turkay Gokgoz, Auteur ; Monika Sester, Auteur Année de publication : 2014 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de Kohonen [Termes IGN] Etats-Unis [Termes IGN] généralisation de base de données [Termes IGN] réseau hydrographique [Termes IGN] réseau neuronal artificiel [Vedettes matières IGN] Généralisation Résumé : (Auteur) In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained. Numéro de notice : A2014-208 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/15230406.2013.877231 En ligne : https://doi.org/10.1080/15230406.2013.877231 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33111 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible