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Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1765 - 1791 Titre : Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience Type de document : Article/Communication Auteurs : Yingwei Yan, Auteur ; Chen-Chieh Feng, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1765 - 1791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées des bénévoles [Termes IGN] GeoWeb [Termes IGN] littérature [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] presse (media) [Termes IGN] problème de Dirichlet [Termes IGN] qualité des données [Termes IGN] recherche [Termes IGN] réseau social [Termes IGN] SIG participatif [Termes IGN] source de données [Termes IGN] système d'information géographique [Termes IGN] Twitter [Termes IGN] utilisation du sol [Termes IGN] Wikimapia Résumé : (auteur) More than 10 years have passed since the coining of the term volunteered geographic information (VGI) in 2007. This article presents the results of a review of the literature concerning VGI. A total of 346 articles published in 24 international refereed journals in GIScience between 2007 and 2017 have been reviewed. The review has uncovered varying levels of popularity of VGI research over space and time, and varying interests in various sources of VGI (e.g. OpenStreetMap) and VGI-related terms (e.g. user-generated content) that point to the multi-perspective nature of VGI. Content-wise, using latent Dirichlet allocation (LDA), this study has extracted 50 specific research topics pertinent to VGI. The 50 topics have been subsequently clustered into 13 intermediate topics and three overarching themes to allow a hierarchical topic review. The overarching VGI research themes include (1) VGI contributions and contributors, (2) main fields applying VGI, and (3) conceptions and envisions. The review of the articles under the three themes has revealed the progress and the points that demand attention regarding the individual topics. This article also discusses the areas that the existing research has not yet adequately explored and proposes an agenda for potential future research endeavors. Numéro de notice : A2020-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730848 Date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95623 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680 Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse multiéchelle [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] modélisation 3D [Termes IGN] Oakland (Californie) [Termes IGN] régression [Termes IGN] semis de points Résumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 Date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711 [article]

Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm / Xiaonan Wang in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur ; Shihong Du, Auteur ; Chen-Chieh Feng, Auteur ; Xueying Zhang, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] langage naturel (informatique) [Termes IGN] relation sémantique [Termes IGN] relation topologique [Termes IGN] toponyme flou Résumé : (Auteur) Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts. Numéro de notice : A2018-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020058 En ligne : https://doi.org/10.3390/ijgi7020058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89533 [article]

A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships / Ick-Hoi Kim in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1042 - 1060 Titre : A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships Type de document : Article/Communication Auteurs : Ick-Hoi Kim, Auteur ; Chen-Chieh Feng, Auteur ; Yi-Chen Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1042 - 1060 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement de données localisées [Termes IGN] axe médian [Termes IGN] base de données historiques [Termes IGN] classification par arbre de décision [Termes IGN] conflation [Termes IGN] distance de Hausdorff [Termes IGN] données anciennes [Termes IGN] objet géographique linéaire [Termes IGN] relation topologique [Termes IGN] réseau routier [Termes IGN] similitude [Termes IGN] valeur moyenne Résumé : (auteur) Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching. Numéro de notice : A2017-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1267736 En ligne : http://dx.doi.org/10.1080/13658816.2016.1267736 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85177 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Classifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 542 - 568 Titre : Classifying natural-language spatial relation terms with random forest algorithm Type de document : Article/Communication Auteurs : Shihong Du, Auteur ; Xiaonan Wang, Auteur ; Chen-Chieh Feng, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 542 - 568 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage dirigé [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] intelligence artificielle [Termes IGN] interface en langage naturel [Termes IGN] langage naturel (informatique) [Termes IGN] méthode robuste [Termes IGN] recherche d'information géographique [Termes IGN] relation spatiale [Termes IGN] relation topologique [Termes IGN] similitude sémantique Résumé : (Auteur) The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies. Numéro de notice : A2017-078 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1212356 En ligne : http://dx.doi.org/10.1080/13658816.2016.1212356 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84340 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Representation and discovery of building patterns: a three-level relational approach / Shihong Du in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

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Integrative representation and inference of qualitative locations about points, lines, and polygons / Shihong Du in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)  

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Combining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)  

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