Descripteur
Documents disponibles dans cette catégorie (40)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
SensePlace3: a geovisual framework to analyze place–time–attribute information in social media / Scott Pezanowski in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)
[article]
Titre : SensePlace3: a geovisual framework to analyze place–time–attribute information in social media Type de document : Article/Communication Auteurs : Scott Pezanowski, Auteur ; Alan M. MacEachren, Auteur ; Alexander Savelyev, Auteur ; Anthony C. Robinson, Auteur Année de publication : 2018 Article en page(s) : pp 420 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatio-temporelle
[Termes IGN] données massives
[Termes IGN] environnement de développement
[Termes IGN] gestion de crise
[Termes IGN] réseau social
[Termes IGN] trace numérique
[Termes IGN] Twitter
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources. Numéro de notice : A2018-272 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2017.1370391 Date de publication en ligne : 11/09/2017 En ligne : https://doi.org/10.1080/15230406.2017.1370391 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90336
in Cartography and Geographic Information Science > Vol 45 n° 5 (August 2018) . - pp 420 - 437[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 032-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Exploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
[article]
Titre : Exploring geo-tagged photos for land cover validation with deep learning Type de document : Article/Communication Auteurs : Hanfa Xing, Auteur ; Yuan Meng, Auteur ; Zixuan Wang, Auteur ; Kaixuan Fan, Auteur ; Dongyang Hou, Auteur Année de publication : 2018 Article en page(s) : pp 237 - 251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'occupation du sol
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] échantillon
[Termes IGN] estimation de précision
[Termes IGN] géobalise
[Termes IGN] image numérique
[Termes IGN] occupation du sol
[Termes IGN] production participative
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Land cover validation plays an important role in the process of generating and distributing land cover thematic maps, which is usually implemented by high cost of sample interpretation with remotely sensed images or field survey. With an increasing availability of geo-tagged landscape photos, the automatic photo recognition methodologies, e.g., deep learning, can be effectively utilised for land cover applications. However, they have hardly been utilised in validation processes, as challenges remain in sample selection and classification for highly heterogeneous photos. This study proposed an approach to employ geo-tagged photos for land cover validation by using the deep learning technology. The approach first identified photos automatically based on the VGG-16 network. Then, samples for validation were selected and further classified by considering photos distribution and classification probabilities. The implementations were conducted for the validation of the GlobeLand30 land cover product in a heterogeneous area, western California. Experimental results represented promises in land cover validation, given that GlobeLand30 showed an overall accuracy of 83.80% with classified samples, which was close to the validation result of 80.45% based on visual interpretation. Additionally, the performances of deep learning based on ResNet-50 and AlexNet were also quantified, revealing no substantial differences in final validation results. The proposed approach ensures geo-tagged photo quality, and supports the sample classification strategy by considering photo distribution, with accuracy improvement from 72.07% to 79.33% compared with solely considering the single nearest photo. Consequently, the presented approach proves the feasibility of deep learning technology on land cover information identification of geo-tagged photos, and has a great potential to support and improve the efficiency of land cover validation. Numéro de notice : A2018-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.025 Date de publication en ligne : 16/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90404
in ISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 237 - 251[article]Réservation
Réserver ce documentExemplaires (3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A framework for annotating OpenStreetMap objects using geo-tagged tweets / Xin Chen in Geoinformatica, vol 22 n° 3 (July 2018)
[article]
Titre : A framework for annotating OpenStreetMap objects using geo-tagged tweets Type de document : Article/Communication Auteurs : Xin Chen, Auteur ; Hoang Vo, Auteur ; Yu Wang, Auteur ; Fusheng Wang, Auteur Année de publication : 2018 Article en page(s) : pp 589 - 613 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] corpus
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] enrichissement sémantique
[Termes IGN] géobalise
[Termes IGN] intégration de données
[Termes IGN] objet géographique
[Termes IGN] OpenStreetMap
[Termes IGN] TwitterRésumé : (Auteur) Recent years have witnessed an explosion of geospatial data, especially in the form of Volunteered Geographic Information (VGI). As a prominent example, OpenStreetMap (OSM) creates a free editable map of the world from a large number of contributors. On the other hand, social media platforms such as Twitter or Instagram supply dynamic social feeds at population level. As much of such data is geo-tagged, there is a high potential on integrating social media with OSM to enrich OSM with semantic annotations, which will complement existing objective description oriented annotations to provide a broader range of annotations. In this paper, we propose a comprehensive framework on integrating social media data and VGI data to derive knowledge about geographical objects, specifically, top relevant annotations from tweets for objects in OSM. We first integrate geo-tagged tweets with OSM data with scalable spatial queries running on MapReduce. We propose a frequency based method for annotating boundary based geographic objects (a polygon), and a probability based method for annotating point based geographic objects (Latitude and Longitude), with consideration of noise. We evaluate our methods using a large geo-tagged tweets corpus and representative geographic objects from OSM, which demonstrates promising results through ground-truth comparison and case studies. We are able to produce up to 80% correct names for geographical objects and discover implicitly relevant information, such as popular exhibitions of a museum, the nicknames or visitors’ impression to a tourism attraction. Numéro de notice : A2018-369 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-018-0323-8 Date de publication en ligne : 20/06/2018 En ligne : https://doi.org/10.1007/s10707-018-0323-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90760
in Geoinformatica > vol 22 n° 3 (July 2018) . - pp 589 - 613[article]Human mobility semantics analysis : a probabilistic and scalable approach / Xiaohui Guo in Geoinformatica, vol 22 n° 3 (July 2018)
[article]
Titre : Human mobility semantics analysis : a probabilistic and scalable approach Type de document : Article/Communication Auteurs : Xiaohui Guo, Auteur ; Richong Zhang, Auteur ; Xudong Liu, Auteur ; Jinpeng Huai, Auteur Année de publication : 2018 Article en page(s) : pp 507 - 539 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées
[Termes IGN] données spatiotemporelles
[Termes IGN] mobilité humaine
[Termes IGN] programmation stochastique
[Termes IGN] segmentation sémantique
[Termes IGN] trace numériqueRésumé : (Auteur) The popularity of smart mobile devices generated data, e.g., check-ins and geo-tagged status, offers new opportunity for better understanding human mobility regularity. Existing works on this problem usually resort to explicit frequency statistics models, such as association rules and sequential patterns, and rely on Euclidean distance to measure the spatial dependence. However, the noisiness and uncertainty natures of geospatial data hinder these methods’ application on human mobility in robust and intuitive way. Moreover, the mobility spatial data volume and accumulation speed challenge the traditional methods in efficiency, scalability, and time-space complexity aspects. In this context, we leverage full Bayesian sequential modeling, to revisit mobility regularity discovery from high level probabilistic semantic knowledge perspective, and to alleviate the inherent in mobility modeling and geo-data noisiness induced uncertainty. Specifically, the mobility semantics is embodied by virtue of underlying geospatial topics and topical transitions of mobility trajectories. A classic variational inference is derived to estimate posterior and predictive probabilities, and furthermore, the stochastic optimization is utilized to mitigate the costly computational overhead in message passing subroutine. The experimental results confirm that our approach not only reasonably recognizes the geospatial mobility semantic patterns, but also scales up well to embrace the massive spatial-temporal human mobility activity data. Numéro de notice : A2018-310 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0295-0 Date de publication en ligne : 10/04/2017 En ligne : https://doi.org/10.1007/s10707-017-0295-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90757
in Geoinformatica > vol 22 n° 3 (July 2018) . - pp 507 - 539[article]Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records / Zhang Liu in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records Type de document : Article/Communication Auteurs : Zhang Liu, Auteur ; Ting Ma, Auteur ; Yunyan Du, Auteur ; Tao Pei, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 494 - 513 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte thématique
[Termes IGN] cartographie des flux
[Termes IGN] classification par réseau neuronal
[Termes IGN] mobilité urbaine
[Termes IGN] population urbaine
[Termes IGN] régression
[Termes IGN] téléphone intelligent
[Termes IGN] trace numérique
[Termes IGN] trajet (mobilité)Résumé : (Auteur) Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation. Numéro de notice : A2018-215 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12323 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1111/tgis.12323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90006
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 494 - 513[article]The characteristics of asymmetric pedestrian behavior : A preliminary study using passive smartphone location data / Nick Malleson in Transactions in GIS, vol 22 n° 2 (April 2018)PermalinkAppariement automatique de données hétérogènes: textes, traces GPS et ressources géographiques / Amine Medad (2018)PermalinkConvolutional neural network for traffic signal inference based on GPS traces / Yann Méneroux (2018)PermalinkDetection and localization of traffic signals with GPS floating car data and Random Forest / Yann Méneroux (2018)PermalinkQue reste-t-il de Friday Harbor ? Pour une approche critique renouvelée des usages du géoweb fondée sur l’analyse des traces numériques / Matthieu Noucher in Revue internationale de géomatique, vol 28 n° 1 (janvier - mars 2018)PermalinkUnveiling movement uncertainty for robust trajectory similarity analysis / Andre Salvaro Furtado in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)PermalinkAn analysis of movement patterns between zones using taxi GPS data / Zhanlong Chen in Transactions in GIS, vol 21 n° 6 (December 2017)PermalinkExtracting spatial patterns in bicycle routes from crowdsourced data / Jody Sultan in Transactions in GIS, vol 21 n° 6 (December 2017)PermalinkSpatiotemporal model for assessing the stability of urban human convergence and divergence patterns / Zhixiang Fang in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)PermalinkAn iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls / A. Mozas-Calvache in Survey review, vol 49 n° 355 (October 2017)PermalinkCrowdsourcing a cyclist perspective on suggested recreational paths in real-world networks / Kevin Baker in Cartography and Geographic Information Science, Vol 44 n° 5 (September 2017)PermalinkDepicting urban boundaries from a mobility network of spatial interactions : a case study of Great Britain with geo-located Twitter data / Junjun Yin in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkIndex-supported pattern matching on tuples of time-dependent values / Fabio Valdés in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkMapping changes of residence with passive mobile positioning data : the case of Estonia / Pilleriine Kamenjuk in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkExtracting urban functional regions from points of interest and human activities on location-based social networks / Song Gao in Transactions in GIS, vol 21 n° 3 (June 2017)PermalinkInformation extraction and visualization from twitter considering spatial structure / Hideyuki Fujita in Cartographica, vol 52 n° 2 (Summer 2017)PermalinkDemand and supply of cultural ecosystem services: Use of geotagged photos to map the aesthetic value of landscapes in Hokkaido / Nobuhiko Yoshimura in Ecosystem Services, vol 24 (April 2017)PermalinkCrowdsourcing functions of the living city from Twitter and Foursquare data / Xiaolu Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)PermalinkBumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas / Colin Robertson in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)PermalinkPosition validation in crowdsourced accessibility mapping / Rebecca M. Rice in Cartographica, vol 51 n° 2 (Summer 2016)Permalink