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Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)
[article]
Titre : Raw GIS to 3D road modeling for real-time traffic simulation Type de document : Article/Communication Auteurs : Yacine Amara, Auteur ; Abdenour Amamra, Auteur ; Salim Khemis, Auteur Année de publication : 2022 Article en page(s) : pp 239 - 256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] comportement
[Termes IGN] graphe topologique
[Termes IGN] intersection spatiale
[Termes IGN] modèle de simulation
[Termes IGN] modélisation 3D
[Termes IGN] navigation virtuelle
[Termes IGN] planification urbaine
[Termes IGN] système d'information géographique
[Termes IGN] système multi-agents
[Termes IGN] temps réel
[Termes IGN] trafic routier
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) In this work, we propose a new approach to road modeling and 3D traffic simulation. Based on the raw geographic information system (GIS) data laid out as sparse polylines with attributes, we compute a more adequate functional description for real-time simulation of on-road vehicle animation. The proposed approach begins with a filtering/subdivision module where the raw polylines are transformed into a graph of functional road segments as arcs and the nodes as intersections. Then, the vehicle speed profile is computed based on its dynamics, its neighborhood and the curvature profile of the road. Afterward, a multi-agent system is proposed in order to handle a large number of simulated vehicle/driver couples. Finally, we deploy a 3D rendering engine to display the computed 3D simulation on screen. The resulting model satisfies most of the real road features for traffic simulation including road interchanges, roundabouts, intersections, lanes, etc. More importantly, the simulated driving qualitatively mimics the real behavior of the drivers/vehicles on the road as can be seen in the accompanying video (RTSP video). We also validate our findings with a technical assessment based on macroscopic and microscopic traffic simulation metrics in several road traffic scenarios. Numéro de notice : A2022-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-020-02013-1 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02013-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99777
in The Visual Computer > vol 38 n° 1 (January 2022) . - pp 239 - 256[article]Recurrent origin–destination network for exploration of human periodic collective dynamics / Xiaojian Chen in Transactions in GIS, vol 26 n° 1 (February 2022)
[article]
Titre : Recurrent origin–destination network for exploration of human periodic collective dynamics Type de document : Article/Communication Auteurs : Xiaojian Chen, Auteur ; Jiayi Xie, Auteur ; Changjiang Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 317 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données localisées dynamiques
[Termes IGN] flux
[Termes IGN] origine - destination
[Termes IGN] planification urbaine
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] taxi
[Termes IGN] Wuhan (Chine)Résumé : (auteur) While daily periodic movements of individuals have been widely studied, their collective dynamics are not understood. To capture periodic collective dynamics, this article represents individual daily movements as a time series of directed weighted origin–destination (OD) networks, and proposes an approach to identify a sub-network called the “recurrent OD network”, which contains frequent edges appearing in each day. Taxi trajectory data over a period of 6 months in Wuhan, China are used for the case study. Here, we extracted the recurrent OD networks for each 2-h period on a given day, and compared them with the corresponding “major OD network” defined by both frequent and infrequent edges. Results show that the recurrent OD networks coincidentally exhibit spatially localized community structures and distinctive patterns of inflow and outflow for each region within a day. Overall, both methodology and findings in this study might make significant contributions in a range of fields, such as urban planning, regional economic development, and infectious disease control. Numéro de notice : A2022-179 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12849 Date de publication en ligne : 05/10/2021 En ligne : https://doi.org/10.1111/tgis.12849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99838
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 317 - 340[article]A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)
[article]
Titre : A robust nonrigid point set registration framework based on global and intrinsic topological constraints Type de document : Article/Communication Auteurs : Guiqiang Yang, Auteur ; Rui Li, Auteur ; Yujun Liu, Auteur ; Ji Wang, Auteur Année de publication : 2022 Article en page(s) : pp 603 - 623 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] contrainte géométrique
[Termes IGN] contrainte topologique
[Termes IGN] descripteur local
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] méthode robuste
[Termes IGN] processus gaussien
[Termes IGN] semis de points
[Termes IGN] superposition de donnéesRésumé : (auteur) The problem of registering nonrigid point sets, with the aim of estimating the correspondences and learning the transformation between two given sets of points, often arises in computer vision tasks. This paper proposes a novel method for performing nonrigid point set registration on data with various types of degradation, in which the registration problem is formulated as a Gaussian mixture model (GMM)-based density estimation problem. Specifically, two complementary constraints are jointly considered for optimization in a GMM probabilistic framework. The first is a thin-plate spline-based regularization constraint that maintains global spatial motion consistency, and the second is a spectral graph-based regularization constraint that preserves the intrinsic structure of a point set. Moreover, the correspondences and the transformation are alternately optimized using the expectation maximization algorithm to obtain a closed-form solution. We first utilize local descriptors to construct the initial correspondences and then estimate the underlying transformation under the GMM-based framework. Experimental results on contour images and real images show the effectiveness and robustness of the proposed method. Numéro de notice : A2022-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02037-7 Date de publication en ligne : 21/02/2022 En ligne : https://doi.org/10.1007/s00371-020-02037-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100040
in The Visual Computer > vol 38 n° 2 (February 2022) . - pp 603 - 623[article]Siamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)
[article]
Titre : Siamese Adversarial Network for image classification of heavy mineral grains Type de document : Article/Communication Auteurs : Huizhen Hao, Auteur ; Zhiwei Jiang, Auteur ; Shiping Ge, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] microscope électronique
[Termes IGN] minéral
[Termes IGN] polarisation croisée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal siamois
[Termes IGN] séparateur à vaste margeRésumé : (auteur) The identification of heavy mineral grains based on microscopic images can significantly reduce the time and economic cost of the identification. There are several deep learning models to realize end-to-end identification of mineral image recently. However, due to the variety and complexity of mineral images, the existing models are difficult to accurately recognize heavy mineral grains in microscopic images. Here we propose the Siamese Adversarial Network (SAN) for image classification of the heavy mineral grains, which is the first time to focus on addressing the domain difference of heavy mineral images from different basins. In more details, we design a Siamese feature encoder to extract features of both the plane-polarized and cross-polarized images as internal representation of heavy mineral grains. The features are reconstructed to discard domain-related information by adversarial training the heavy mineral classifier and domain discriminator. The identification performance of the models under the three mixed domain experiments is consistently higher than the performance under the same domain settings respectively which shows that the model we proposed achieves a great generalization ability on unseen domains. Numéro de notice : A2022-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.105016 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.105016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99810
in Computers & geosciences > vol 159 (February 2022) . - n° 105016[article]Spatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
[article]
Titre : Spatiotemporal temperature fusion based on a deep convolutional network Type de document : Article/Communication Auteurs : Xuehan Wang, Auteur ; Zhenfeng Shao, Auteur ; Xiao Huang, Auteur ; Deren Li, Auteur Année de publication : 2022 Article en page(s) : pp 93 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion de données multisource
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] réseau neuronal convolutif
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] température de surfaceRésumé : (Auteur) High-spatiotemporal-resolution land surface temperature (LST) images are essential in various fields of study. However, due to technical constraints, sensing systems have difficulty in providing LSTs with both high spatial and high temporal resolution. In this study, we propose a multi-scale spatiotemporal temperature-image fusion network (MSTTIFN) to generate high-spatial-resolution LST products. The MSTTIFN builds nonlinear mappings between the input Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs and the out- put Landsat LSTs at the target date with two pairs of references and therefore enhances the resolution of time-series LSTs. We conduct experiments on the actual Landsat and MODIS data in two study areas (Beijing and Shandong) and compare our proposed MSTTIFN with four competing methods: the Spatial and Temporal Adaptive Reflectance Fusion Model, the Flexible Spatiotemporal Data Fusion Model, a two-stream convolutional neural network (StfNet), and a deep learning-based spatiotemporal temperature-fusion network. Results reveal that the MSTTIFN achieves the best and most stable performance. Numéro de notice : A2022-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00023R2 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00023R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99724
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 93 - 101[article]Réservation
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