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Delivering time-evolving 3D city models for web visualization / Vincent Jaillot in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
[article]
Titre : Delivering time-evolving 3D city models for web visualization Type de document : Article/Communication Auteurs : Vincent Jaillot, Auteur ; Sylvie Servigne, Auteur ; Gilles Gesquière, Auteur Année de publication : 2020 Article en page(s) : pp 2030 - 2052 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] CityGML
[Termes IGN] implémentation (informatique)
[Termes IGN] interactivité
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle conceptuel de données
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] Open geospatial consortiumRésumé : (auteur) Studying and planning urban evolution is essential for understanding the past and designing the cities of the future and can be facilitated by providing means for sharing, visualizing, and navigating in cities, on the web, in space and in time. Standard formats, methods, and tools exist for visualizing large-scale 3D cities on the web. In this paper, we go further by integrating the temporal dimension of cities in geospatial web delivery standard formats. In doing so, we enable interactive visualization of large-scale time-evolving 3D city models on the web. A key characteristic of this paper lies in the proposed four-step generic approach. First, we design a generic conceptual model of standard formats for delivering 3D cities on the web. Then, we formalize and integrate the temporal dimension of cities into this generic conceptual model. Following which, we specify the conceptual model in the 3D Tiles standard at logical and technical specification levels, resulting in an extension of 3D Tiles for delivering time-evolving 3D city models on the web. Finally, we propose an open-source implementation, experiments, and an evaluation of the propositions and visualization rules. We also provide access to reproducibility notes allowing researchers to replicate all the experiments. Numéro de notice : A2020-514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1749637 Date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1749637 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95673
in International journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 2030 - 2052[article]From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity / Elham Naghizade in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)
[article]
Titre : From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity Type de document : Article/Communication Auteurs : Elham Naghizade, Auteur ; jeffrey Chan, Auteur ; Martin Tomko, Auteur Année de publication : 2020 Article en page(s) : pp 2004 - 2029 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] comportement
[Termes IGN] données GPS
[Termes IGN] données spatiotemporelles
[Termes IGN] échantillonnage
[Termes IGN] gestion de trafic
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] origine - destination
[Termes IGN] trajet (mobilité)Résumé : (auteur) The ubiquity of personal sensing devices has enabled the collection of large, diverse, and fine-grained spatio-temporal datasets. These datasets facilitate numerous applications from traffic monitoring and management to location-based services. Recently, there has been an increasing interest in profiling individuals' movements for personalized services based on fine-grained trajectory data. Most approaches identify the most representative paths of a user by analyzing coarse location information, e.g., frequently visited places. However, even for trips that share the same origin and destination, individuals exhibit a variety of behaviors (e.g., a school drop detour, a brief stop at a supermarket). The ability to characterize and compare the variability of individuals' fine-grained movement behavior can greatly support location-based services and smart spatial sampling strategies. We propose a TRip DIversity Measure --TRIM – that quantifies the regularity of users' path choice between an origin and destination. TRIM effectively captures the extent of the diversity of the paths that are taken between a given origin and destination pair, and identifies users with distinct movement patterns, while facilitating the comparison of the movement behavior variations between users. Our experiments using synthetic and real datasets and across geographies show the effectiveness of our method. Numéro de notice : A2020-512 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1730849 Date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1730849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95666
in International journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 2004 - 2029[article]Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)
[article]
Titre : Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data Type de document : Article/Communication Auteurs : Yaotong Cai, Auteur ; Xinyu Li, Auteur ; Meng Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] algorithme de généralisation
[Termes IGN] analyse d'image orientée objet
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] rétrodiffusion
[Termes IGN] série temporelle
[Termes IGN] zone humideRésumé : (auteur) Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas. Numéro de notice : A2020-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102164 Date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102164 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96398
in International journal of applied Earth observation and geoinformation > vol 92 (October 2020) . - n° 102164[article]An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
[article]
Titre : An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Changxiu Cheng, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2020 Article en page(s) : pp 1822 - 1848 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] classification barycentrique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] exploration de données
[Termes IGN] géoréférencement
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] taxinomieRésumé : (auteur) Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. Numéro de notice : A2020-477 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726922 Date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95624
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1822 - 1848[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
[article]
Titre : A lightweight ensemble spatiotemporal interpolation model for geospatial data Type de document : Article/Communication Auteurs : Shifen Cheng, Auteur ; Peng Peng, Auteur ; Feng Lu, Auteur Année de publication : 2020 Article en page(s) : pp 1849 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] coefficient de corrélation
[Termes IGN] distance pondérée
[Termes IGN] données localisées
[Termes IGN] erreur absolue
[Termes IGN] interpolation spatiale
[Termes IGN] lissage de données
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] requête spatiotemporelleRésumé : (auteur) Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations. Numéro de notice : A2020-484 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725016 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95651
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1849 - 1872[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Measuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)PermalinkComputational improvements to multi-scale geographically weighted regression / Ziqi Li in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)PermalinkIntegration of spatialization and individualization: the future of epidemic modelling for communicable diseases / Meifang Li in Annals of GIS, vol 26 n° 3 (July 2020)PermalinkSpatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships / Chao Song in Annals of GIS, vol 26 n° 3 (July 2020)PermalinkMining spatiotemporal association patterns from complex geographic phenomena / Zhanjun He in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkA multi-factor spatial optimization approach for emergency medical facilities in Beijing / Liang Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkA 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)PermalinkA spatio-temporal deformation model for laser scanning point clouds / Corinna Harmening in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkA polyhedra-based model for moving regions in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)PermalinkMulti‐temporal transport network models for accessibility studies / Diego Bogado Tomasiello in Transactions in GIS, vol 23 n° 2 (April 2019)Permalink