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The use of geometric indicators to estimate the quantitative completeness of street blocks in OpenStreetMap / Qi Zhou in Transactions in GIS, vol 22 n° 6 (December 2018)
[article]
Titre : The use of geometric indicators to estimate the quantitative completeness of street blocks in OpenStreetMap Type de document : Article/Communication Auteurs : Qi Zhou, Auteur ; YuanJian Tian, Auteur Année de publication : 2018 Article en page(s) : pp 1550 - 1572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] données localisées des bénévoles
[Termes IGN] exhaustivité des données
[Termes IGN] îlot urbain
[Termes IGN] indicateur spatial
[Termes IGN] OpenStreetMap
[Termes IGN] qualité des donnéesRésumé : (Auteur) OpenStreetMap (OSM) is a free global map dataset that was created by volunteers around the world. This inevitably means that there are a number of quality issues with the final OSM product, however. Extensive research has therefore been carried out to assess the quality of this product by applying a range of measures versus reference datasets, but little effort to date has been focused on quantitative quality estimation without reference datasets. The aim of this study is therefore to quantitatively estimate the completeness of street blocks in an OSM dataset. This was accomplished by initially exploring the relationship between geometric indicators (i.e., area, perimeter, and density) and street block completeness in an OSM road dataset, before these relationships were applied to quantitatively estimate completeness in other datasets. The results of this study show that: (1) street block completeness is positively correlated with density and negatively correlated with area and perimeter; and (2) in most cases, estimated completeness values for all street blocks within an OSM road dataset do not differ by more than 10% in absolute terms from actual completeness values. These results indicate that geometric indicators can be used as proxies to quantitatively estimate road completeness in OSM datasets. Numéro de notice : A2018-568 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12486 Date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1111/tgis.12486 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92287
in Transactions in GIS > vol 22 n° 6 (December 2018) . - pp 1550 - 1572[article]Urban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)
[article]
Titre : Urban impervious surface estimation from remote sensing and social data Type de document : Article/Communication Auteurs : Yan Yu, Auteur ; Jun Li, Auteur ; Changyu Zhu, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2018 Article en page(s) : pp 771 - 780 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] base de données routières
[Termes IGN] Canton (Kouangtoung)
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données vectorielles
[Termes IGN] Google Maps
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] OpenStreetMap
[Termes IGN] point d'intérêt
[Termes IGN] régression multiple
[Termes IGN] réseau routier
[Termes IGN] surface imperméable
[Termes IGN] zone urbaineRésumé : (auteur) We propose an inspiring approach for accurate impervious surface estimation based on the integration of remote sensing and social data. The proposed approach exploits the strengths of two kind of heterogeneous features, i.e., physical features and social features, where the former ones are derived by a morphological attribute profiles-guided spectral mixture analysis model using remote sensing imagery, and the latter ones are obtained from the normalized kernel density of point of interest and vector road datasets. These two features are then integrated using a multivariable linear regression model to estimate impervious surfaces. The proposed method has been tested in the main urban area of Guangzhou, China, in pixel level and parcel level, respectively. The obtained results, with the overall RMSE of 10.98% and 10.90% for pixel level and parcel level, respectively, demonstrate the good performance of integrating remote sensing imagery and social data for mapping of urban impervious surface. Numéro de notice : A2018-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.12.771 Date de publication en ligne : 01/12/2018 En ligne : https://doi.org/10.14358/PERS.84.12.771 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91622
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 12 (December 2018) . - pp 771 - 780[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2018121 RAB Revue Centre de documentation En réserve L003 Disponible A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks / Lin Wan in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)
[article]
Titre : A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks Type de document : Article/Communication Auteurs : Lin Wan, Auteur ; Yuming Hong, Auteur ; Zhou Huang, Auteur ; Xia Peng, Auteur ; Ran Li, Auteur Année de publication : 2018 Article en page(s) : pp 2225 - 2246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] calcul d'itinéraire
[Termes IGN] classification bayesienne
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] données météorologiques
[Termes IGN] exploration de données géographiques
[Termes IGN] géobalise
[Termes IGN] image Flickr
[Termes IGN] Pékin (Chine)
[Termes IGN] point d'intérêtRésumé : (Auteur) Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns. Numéro de notice : A2018-523 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458988 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91348
in International journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2225 - 2246[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018061 RAB Revue Centre de documentation En réserve L003 Disponible On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)
[article]
Titre : On the spatial distribution of buildings for map generalization Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Qingsheng Guo, Auteur ; Lin Wang, Auteur ; Fen Yan, Auteur Année de publication : 2018 Article en page(s) : pp 539 - 555 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] arbre aléatoire minimum
[Termes IGN] bati
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] OpenStreetMap
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. Numéro de notice : A2018-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1433068 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1433068 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91258
in Cartography and Geographic Information Science > Vol 45 n° 6 (November 2018) . - pp 539 - 555[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Toward a participatory VGI methodology : crowdsourcing information on regional food assets / Victoria Fast in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)
[article]
Titre : Toward a participatory VGI methodology : crowdsourcing information on regional food assets Type de document : Article/Communication Auteurs : Victoria Fast, Auteur ; Claus Rinner, Auteur Année de publication : 2018 Article en page(s) : pp 2209 - 2224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] alimentation
[Termes IGN] données localisées des bénévoles
[Termes IGN] exploitation agricole
[Termes IGN] participation du public
[Termes IGN] production participative
[Termes IGN] SIG participatifRésumé : (Auteur) Local knowledge has been underrepresented in food-related policies and planning. The goal of this research was to engage members of a local food community and generate volunteered geographic information (VGI) on community food assets. During active data collection, over 200 food assets were reported. This paper details the systematic approach used to create VGI, which emphasizes the socio-cultural context surrounding the mapping technology. The project began with an identified need to connect to and learn from the local food community. The participants were drawn from active food system stakeholders, and a Geoweb infrastructure was selected based on publicly available crowdsourcing tools. The resulting VGI is presented according to system functions: input (Web traffic, contributors, input types), management (contribution vetting, privacy), analysis (typology of input), and presentation (sharing the submitted data). Despite limitations, this study revealed a hyper-local and community-driven perspective on food assets, opened access to government and private data, and increased the transparency and accessibility of information on the regional food system. This research also revealed that there is a growing need for intermediaries who can bridge the gap between experts in the subject matter and experts in digitally enabled participation, and a need for non-government open data repositories. Numéro de notice : A2018-522 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1480784 Date de publication en ligne : 05/06/2018 En ligne : https://doi.org/10.1080/13658816.2018.1480784 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91347
in International journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2209 - 2224[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018061 RAB Revue Centre de documentation En réserve L003 Disponible Analyzing the effect of earthquakes on OpenStreetMap contribution patterns and tweeting activities / Ahmed Ahmouda in Geo-spatial Information Science, vol 21 n° 3 (October 2018)PermalinkA cross-analysis framework for multi-source volunteered, crowdsourced, and authoritative geographic information : The case study of volunteered personal traces analysis against transport network data / Gloria Bordogna in Geo-spatial Information Science, vol 21 n° 3 (October 2018)PermalinkData trustworthiness and user reputation as indicators of VGI quality / Paolo Fogliaroni in Geo-spatial Information Science, vol 21 n° 3 (October 2018)PermalinkOpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project / Amin Mobasheri in Geo-spatial Information Science, vol 21 n° 3 (October 2018)PermalinkAn experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency / Ana-Maria Olteanu-Raimond in Land, vol 7 n° 3 (September 2018)PermalinkDiversity and transparency in (volunteered) geoinformation practices / Tilo Felgenhauer in GI Forum, vol 2018 n° 2 ([01/09/2018])PermalinkPedestrian network information extraction based on VGI / Xuejing Xie in Geomatica, vol 72 n° 3 (September 2018)PermalinkShare our cultural heritage (SOCH) : worldwide 3D heritage reconstruction and visualization via Web and mobile GIS / Hari K. Dhonju in ISPRS International journal of geo-information, vol 7 n° 9 (September 2018)PermalinkA framework for annotating OpenStreetMap objects using geo-tagged tweets / Xin Chen in Geoinformatica, vol 22 n° 3 (July 2018)PermalinkThe life cycle of contributors in collaborative online communities -the case of OpenStreetMap / Daniel Begin in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)Permalink