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Relative space-based GIS data model to analyze the group dynamics of moving objects / Mingxiang Feng in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 74 - 95 Titre : Relative space-based GIS data model to analyze the group dynamics of moving objects Type de document : Article/Communication Auteurs : Mingxiang Feng, Auteur ; Shih-Lung Shaw, Auteur ; Zhixiang Fang, Auteur ; Hao Cheng, Auteur Année de publication : 2019 Article en page(s) : pp 74 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] base de données orientée objet [Termes descripteurs IGN] modèle conceptuel de données [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] SIG dynamique [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] UML Résumé : (Auteur) The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects’ relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5–50% those of the absolute GIS method for the same function of these three scenarios. It’s better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service. Numéro de notice : A2019-261 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.002 date de publication en ligne : 15/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93074 [article]

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BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 157 - 171 Titre : BIM-Tracker: A model-based visual tracking approach for indoor localisation using a 3D building model Type de document : Article/Communication Auteurs : Debaditya Acharya, Auteur ; Milad Ramezani, Auteur ; Kourosh Khoshelham, Auteur ; Stephan Winter, Auteur Année de publication : 2019 Article en page(s) : pp 157 - 171 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] algorithme de Gauss-Newton [Termes descripteurs IGN] appariement de données localisées [Termes descripteurs IGN] corrélation à l'aide de traits caractéristiques [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] longueur focale [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] modèle 3D du site [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] trajectoire Résumé : (Auteur) This article presents an accurate and robust visual indoor localisation approach that not only is infrastructure-free, but also avoids accumulation error by taking advantage of (1) the widespread ubiquity of mobile devices with cameras and (2) the availability of 3D building models for most modern buildings. Localisation is performed by matching image sequences captured by a camera, with a 3D model of the building in a model-based visual tracking framework. Comprehensive evaluation of the approach with a photo-realistic synthetic dataset shows the robustness of the localisation approach under challenging conditions. Additionally, the approach is tested and evaluated on real data captured by a smartphone. The results of the experiments indicate that a localisation accuracy better than 10 cm can be achieved by using this approach. Since localisation errors do not accumulate the proposed approach is suitable for indoor localisation tasks for long periods of time and augmented reality applications, without requiring any local infrastructure. A MATLAB implementation can be found on https://github.com/debaditya-unimelb/BIM-Tracker. Numéro de notice : A2019-139 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.014 date de publication en ligne : 27/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92473 [article]

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A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 833 - 854 Titre : A methodology with a distributed algorithm for large-scale trajectory distribution prediction Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2019 Article en page(s) : pp 833 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] circulation urbaine [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] gestion de trafic [Termes descripteurs IGN] migration pendulaire [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] trajectoire Résumé : (Auteur) In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. Numéro de notice : A2019-218 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1536981 date de publication en ligne : 31/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1536981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92690 [article]

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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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2225 - 2246 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 descripteurs IGN] calcul d'itinéraire [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] image Flickr [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] trajectoire Résumé : (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 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 [article]

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Mining and visual exploration of closed contiguous sequential patterns in trajectories / Can Yang in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 7-8 (July - August 2018) . - pp 1413 - 1435 Titre : Mining and visual exploration of closed contiguous sequential patterns in trajectories Type de document : Article/Communication Auteurs : Can Yang, Auteur ; Gyözö Gidofalvi, Auteur Année de publication : 2018 Article en page(s) : pp 1413 - 1435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] arbre de décision [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] visualisation de données [Vedettes matières IGN] Géovisualisation Mots-clés libres : closed contiguous sequential pattern Résumé : (auteur) Large collections of trajectories provide rich insight into movement patterns of the tracked objects. By map matching trajectories to a road network as sequences of road edge IDs, contiguous sequential patterns can be extracted as a certain number of objects traversing a specific path, which provides valuable information in travel demand modeling and transportation planning. Mining and visualization of such patterns still face challenges in efficiency, scalability, and visual cluttering of patterns. To address these challenges, this article firstly proposes a Bidirectional Pruning based Closed Contiguous Sequential pattern Mining (BP-CCSM) algorithm. By employing tree structures to create partitions of input sequences and candidate patterns, closeness can be checked efficiently by comparing nodes in a tree. Secondly, a system called Sequential Pattern Explorer for Trajectories (SPET) is built for spatial and temporal exploration of the mined patterns. Two types of maps are designed where a conventional traffic map gives an overview of the movement patterns and a dynamic offset map presents detailed information according to user-specified filters. Extensive experiments are performed in this article. BP-CCSM is compared with three other state-of-the-art algorithms on two datasets: a small public dataset containing clickstreams from an e-commerce and a large global positioning system dataset with more than 600,000 taxi trip trajectories. The results show that BP-CCSM considerably outperforms three other algorithms in terms of running time and memory consumption. Besides, SPET provides an efficient and convenient way to inspect spatial and temporal variations in closed contiguous sequential patterns from a large number of trajectories. Numéro de notice : A2018-279 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658816.2017.1393542 date de publication en ligne : 31/10/2017 En ligne : https://doi.org/10.1080/13658816.2017.1393542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90361 [article]

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Un modèle spatiotemporel sémantique pour la modélisation de mobilités en milieu urbain / Meihan Jin in Revue internationale de géomatique, vol 28 n° 3 (juillet - septembre 2018)  

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Using interactions and dynamics for mining groups of moving objects from trajectory data / Corrado Loglisci in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)  

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Attribute trajectory analysis : a framework to analyse attribute changes using trajectory analysis techniques / Long Zhang in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

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Spatial big data and machine learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 28 August 2018 / Martin Raubal (2018)    

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A viewpoint based approach to the visual exploration of trajectory / Jie Li in Journal of Visual Languages and Computing, vol 41 (August 2017)  

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Vision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)  

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A frequency-domain imaging algorithm for highly squinted SAR mounted on maneuvering platforms with nonlinear trajectory / Zhenyu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

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Trajectoires d’objets mobiles dans un espace support fixe / Elodie Buard in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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La mission Rosetta et le calcul de gravité / Julien Laurent-Varin in XYZ, n° 142 (mars - mai 2015)

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Visualization, statistical analysis, and mining of historical vessel data / Sabarish Senthilnathan Muthu (2015)  

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