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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 descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] GeoWeb [Termes descripteurs IGN] littérature [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] presse (media) [Termes descripteurs IGN] problème de Dirichlet [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] recherche [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] SIG participatif [Termes descripteurs IGN] source de données [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] Twitter [Termes descripteurs IGN] utilisation du sol [Termes descripteurs 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 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]

Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 252 - 262 Titre : Water level prediction from social media images with a multi-task ranking approach Type de document : Article/Communication Auteurs : P. Chaudhary, Auteur ; Stefano D'Aronco, Auteur ; João P. Leitão, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 252 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] inondation [Termes descripteurs IGN] niveau hydrostatique [Termes descripteurs IGN] régression [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with 11 cm root mean square error. Numéro de notice : A2020-549 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.003 date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95776 [article]

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Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 41-51 Titre : Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique Type de document : Article/Communication Auteurs : Hao Li, Auteur ; Benjamin Herfort, Auteur ; Wei Huang, Auteur Année de publication : 2020 Article en page(s) : pp 41-51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte sanitaire [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] géographie sociale [Termes descripteurs IGN] inventaire du bâti [Termes descripteurs IGN] Mozambique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] Twitter Résumé : (auteur) Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method. Numéro de notice : A2020-350 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.007 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95233 [article]

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Advancing the theory and practice of system evaluation: a case study in geovisual analytics of social media / Alexander Savelyev in International journal of cartography, Vol 6 n° 2 (July 2020)  

Public [article]  inInternational journal of cartography > Vol 6 n° 2 (July 2020) . - pp 202 - 221 Titre : Advancing the theory and practice of system evaluation: a case study in geovisual analytics of social media Type de document : Article/Communication Auteurs : Alexander Savelyev, Auteur ; Alan M. MacEachren, Auteur Année de publication : 2020 Article en page(s) : pp 202 - 221 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] approche participative [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] logiciel de visualisation [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] utilisateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This paper advances the state-of-the-art in methodology design for empirical evaluation of (geo)visual analytics software. Specifically, we describe the process of design, development and application of a prototypical user study tailored to the evaluation of complex geovisual analytics tools that focus on social media analysis. We fist perform a synthesis of existing theory and best practices for software evaluation of comparable systems. We then demonstrate how the product of said synthesis – a methodological ‘check list’ – can be used to inform a proof-of-concept user study of an actual geovisual analytics software system. The resulting user study design accommodates for the use of real geographic social media datasets, the complexity of the intended analytical process, and for the learning challenges faced by the participants working with a fully-functional and mature geovisual analytics application, and is likely representative of a wide range of evaluation scenarios in (geo)visual analytics. A complete summary of all the study instruments is included to encourage their scrutiny, reuse and modification by others. Finally, we have discovered that participants’ curiosity and desire for autonomy played a noticeable role in the evaluation process – something not previously reported. Numéro de notice : A2020-373 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1637488 date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1080/23729333.2019.1637488 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95305 [article]

Behavior-based location recommendation on location-based social networks / Seyyed Mohammadreza Rahimi in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 3 (July 2020) . - pp 477 – 504 Titre : Behavior-based location recommendation on location-based social networks Type de document : Article/Communication Auteurs : Seyyed Mohammadreza Rahimi, Auteur ; Behrouz Far, Auteur ; Xin Wang, Auteur Année de publication : 2020 Article en page(s) : pp 477 – 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] interface web [Termes descripteurs IGN] modèle conceptuel de données localisées [Termes descripteurs IGN] réseau social géodépendant [Termes descripteurs IGN] système de recommandation Résumé : (auteur) Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However, the existing methods suffer from one or more deficiencies such as data sparsity, cold-start users, ignoring users’ specific spatial and temporal behaviors, not utilizing the shared behaviors of the users. In this paper, we propose a novel location recommendation method, namely Behavior-based Location Recommendation (BLR). BLR recommends a location to a user based on the users’ repetitive behaviors and behaviors of similar users. Additionally, to better integrate the spatial information, BLR has two spatial components, a user-based spatial component to find the spatial preferences of the user, and a behavior-based spatial component to find locations of interest for different behaviors. Experimental studies on three real-world datasets show that BLR produces better location recommendations and can effectively address data sparsity and cold-start problems. Numéro de notice : A2020-370 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-019-00360-3 date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1007/s10707-019-00360-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95265 [article]

Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

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Micro diagrams: visualization of categorical point data from location-based social media / Mathias Gröbe in Cartography and Geographic Information Science, Vol 47 n° 4 (July 2020)  

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Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages / Jimin Wang in Transactions in GIS, Vol 24 n° 3 (June 2020)  

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Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

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A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Online flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica [en ligne], vol 24 n° 2 (April 2020)  

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Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

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Modelling perceived risks to personal privacy from location disclosure on online social networks / Fatma S. Alrayes in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)  

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Volunteered geographic information systems: Technological design patterns / Jose Pablo Gómez‐Barrón in Transactions in GIS, Vol 23 n° 5 (October 2019)  

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