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Improving human mobility identification with trajectory augmentation / Fan Zhou in Geoinformatica, vol 25 n° 3 (July 2021)
[article]
Titre : Improving human mobility identification with trajectory augmentation Type de document : Article/Communication Auteurs : Fan Zhou, Auteur ; Ruiyang Yin, Auteur ; Goce Trajcevski, Auteur ; Kunpeng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 453 - 483 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] itinéraire
[Termes IGN] mobilité humaine
[Termes IGN] modèle numérique de déplacement
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal récurrent
[Termes IGN] utilisateurRésumé : (auteur) Many location-based social networks (LBSNs) applications such as customized Point-Of-Interest (POI) recommendation, preference-based trip planning, travel time estimation, etc., involve an important task of understanding human trajectory patterns. In particular, identifying and linking trajectories to users who generate them – a problem called Trajectory-User Linking (TUL) – has become a focus of many recent works. TUL is usually studied as a multi-class classification problem and has gained recent attention because: (1) the number of labels/classes (i.e., users) is way larger than the number of motion patterns among various trajectories; and (2) the location-based trajectory data, especially the check-ins – i.e., events of reporting a location at particular Point of Interest (POI) with known semantics – are often extremely sparse. Towards addressing these challenges, we introduce a Trajectory Generative Adversarial Network (TGAN) as an approach to enable learning users motion patterns and location distribution, and to eventually identify human mobility. TGAN consists of two jointly trained neural networks, playing a Minimax game to (iteratively) optimize both components. The first one is the generator, learning trajectory representation by a Recurrent Neural Network (RNN) based model, aiming at fitting the underlying trajectory distribution of a particular individual and generate synthetic trajectories with intrinsic invariance and global coherence. The second one is the discriminator – a Convolutional Neural Network (CNN) based model that discriminates the generated trajectory from the real ones and provides guidance to train the generator model. We demonstrate that the above two models can be well tuned together to improve the TUL performance, while achieving superior accuracy when compared to existing approaches. Numéro de notice : A2021-972 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-019-00378-7 Date de publication en ligne : 29/08/2019 En ligne : https://doi.org/10.1007/s10707-019-00378-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100390
in Geoinformatica > vol 25 n° 3 (July 2021) . - pp 453 - 483[article]A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
[article]
Titre : A scalable method to construct compact road networks from GPS trajectories Type de document : Article/Communication Auteurs : Yuejun Guo, Auteur ; Anton Bardera, Auteur ; Marta Fort, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1309 - 1345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] chevauchement
[Termes IGN] compensation par faisceaux
[Termes IGN] contour
[Termes IGN] généralisation automatique de données
[Termes IGN] méthode heuristique
[Termes IGN] noeud
[Termes IGN] réseau routier
[Termes IGN] segmentation par décomposition-fusion
[Termes IGN] squelettisation
[Termes IGN] trajectographie par GPS
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) The automatic generation of road networks from GPS tracks is a challenging problem that has been receiving considerable attention in the last years. Although dozens of methods have been proposed, current techniques suffer from two main shortcomings: the quality of the produced road networks is still far from those produced manually, and the methods are slow, making them not scalable to large inputs. In this paper, we present a fast four-step density-based approach to construct a road network from a set of trajectories. A key aspect of our method is the use of an improved version of the Slide method to adjust trajectories to build a more compact density surface. The network has comparable or better quality than that of state-of-the-art methods and is simpler (includes fewer nodes and edges). Furthermore, we also propose a split-and-merge strategy that allows splitting the data domain into smaller regions that can be processed independently, making the method scalable to large inputs. The performance of our method is evaluated with extensive experiments on urban and hiking data. Numéro de notice : A2021-447 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1832229 Date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/13658816.2020.1832229 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97859
in International journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1309 - 1345[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2021071 SL Revue Centre de documentation Revues en salle Disponible A framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)
[article]
Titre : A framework to manage uncertainty in the computation of waste collection routes after a flood Type de document : Article/Communication Auteurs : Arnaud Le Guilcher , Auteur ; Sofiane Martel, Auteur ; Mickaël Brasebin , Auteur ; Yann Méneroux , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 4 Article en page(s) : pp 61 - 68 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] cadre conceptuel
[Termes IGN] calcul d'itinéraire
[Termes IGN] catastrophe naturelle
[Termes IGN] collecte des déchets
[Termes IGN] discrétisation spatiale
[Termes IGN] incertitude géométrique
[Termes IGN] inondation
[Termes IGN] programmation stochastique
[Termes IGN] variable aléatoireRésumé : (auteur) In this paper, we describe a framework to find a good quality waste collection tour after a flood, without having to solve a complicated optimization problem from scratch in limited time. We model the computation of a waste collection tour as a capacitated routing problem, on the vertices or on the edges of a graph, with uncertain waste quantities and uncertain road availability. Multiple models have been conceived to manage uncertainty in routing problems, and we build on the ideas of discretizing the uncertain parameters and computing master solutions that can be adapted to propose an original method to compute efficient solutions. We first introduce our model for the progressive removal of the uncertainty, then outline our method to compute solutions: our method first considers a low-dimensional set of random variables that govern the behaviour of the problem parameters, discretizes these variables and computes a solution for each discrete point before the flood, and then uses these solutions as a basis to build operational solutions when there are enough information about the parameters of the routing problem. We then give computational tools to implement this method. We give a framework to compute the basis of solutions in an efficient way, by computing all the solutions simultaneously and sharing information (that can lead to good quality solutions) between the different problems based on how close their parameters are, and we also describe how real solutions can be derived from this basis. Our main contributions are our model for the progressive removal of uncertainty, our multi-step method to compute efficient solutions, and our intrusive framework to compute solutions on the discrete grid of parameters. Numéro de notice : A2021-316 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-61-2021 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2021-61-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97946
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2021 (July 2021) . - pp 61 - 68[article]Dynamic optimization models for displaying outdoor advertisement at the right time and place / Meng Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)
[article]
Titre : Dynamic optimization models for displaying outdoor advertisement at the right time and place Type de document : Article/Communication Auteurs : Meng Huang, Auteur ; Zhixiang Fang, Auteur ; Robert Weibel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1179 - 1204 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Chine
[Termes IGN] espace public
[Termes IGN] modèle dynamique
[Termes IGN] optimisation spatiale
[Termes IGN] point d'intérêt
[Termes IGN] publicité
[Termes IGN] téléphonie mobileRésumé : (auteur) Digital billboards, as a new form of outdoor advertising, has gained popularity in recent years per its revolutionized way to control when and where the specific ads appear. However, this development also demands more complicated optimization for strategic deployments: the advertisers have to not only decide on a set of locations to display their ads, but also when to display them. The existing static optimization approaches become insufficient for this dynamic scenario to match advertisement and intended audience. Therefore, this research proposes three models in a workflow to mine mobile phone data and points of interest (POIs) data and to meet advertising needs in various situations. The three optimization models include a dynamic audience model to maximize the coverage of the target users, a dynamic environment model to maximize the coverage of the target environment, and a dynamic integrated model to maximize the coverage of both target audience and environment. A case study using shopping ads in Wuxue, China tests the three optimalization models. The results show that the proposed models are effective for providing an optimal solution for digital billboard configuration with a greater coverage of the target audience and environment compared to the state-of-the-art static models. Numéro de notice : A2021-386 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1823396 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/13658816.2020.1823396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97643
in International journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1179 - 1204[article]Indoor mapping and modeling by parsing floor plan images / Yijie Wu in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)
[article]
Titre : Indoor mapping and modeling by parsing floor plan images Type de document : Article/Communication Auteurs : Yijie Wu, Auteur ; Jianga Shang, Auteur ; Pan Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1205 - 1231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte d'intérieur
[Termes IGN] chevauchement
[Termes IGN] CityGML
[Termes IGN] construction
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] intégrité topologique
[Termes IGN] mur
[Termes IGN] optimisation spatiale
[Termes IGN] positionnement en intérieur
[Termes IGN] vectorisationRésumé : (auteur) A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). Numéro de notice : A2021-385 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1781130 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1781130 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97642
in International journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1205 - 1231[article]A topology-preserving simplification method for 3D building models / Biao Wang in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkEvaluating PPGIS usability in a multi-national field study combining qualitative surveys and eye-tracking / Mona Bartling in Cartographic journal (the), vol 58 n° 2 (May 2021)PermalinkAn integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkRoom semantics inference using random forest and relational graph convolutional networks: A case study of research building / Xuke Hu in Transactions in GIS, Vol 25 n° 1 (February 2021)PermalinkPermalinkAnalysing 18th century hydrographic data: a campaign in the Bay of Biscay, 1750-1751 / Helen Mair Rawsthorne (2021)PermalinkCombining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)PermalinkPermalinkLes impacts spatiaux du changement climatique / Denis Mercier (2021)PermalinkIntégration et analyse de données massives et hétérogènes pour une observation intelligente du territoire / Rodrigue Kafando (2021)Permalink