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Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations / Ruozhen Cheng in Transactions in GIS, vol 26 n° 1 (February 2022)
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Titre : Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations Type de document : Article/Communication Auteurs : Ruozhen Cheng, Auteur ; Jiaxin Liao, Auteur ; Jing Chen, Auteur Année de publication : 2022 Article en page(s) : pp 129 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] appariement d'adresses
[Termes IGN] information sémantique
[Termes IGN] modèle d'ontologie
[Termes IGN] point d'intérêt
[Termes IGN] raisonnement spatial
[Termes IGN] relation spatiale
[Termes IGN] service fondé sur la position
[Termes IGN] similitude sémantiqueRésumé : (auteur) Locating points of interest (POIs) from descriptions can support intelligent location-based services. Available research achieves it through address matching and spatial reasoning. However, semantic characteristics and spatial proximities of address fields are usually neglected in address matching; current applications of spatial reasoning represent qualitative spatial relations in semantic networks for efficient queries, but they do not yet scale to large datasets for qualitative direction reasoning due to massive qualitative direction relations between objects; moreover, spatial reasoning on various quantitative distances should be optimized. This study proposes a method that improves the accuracy of address matching by combining multiple similarities and enables quick spatial reasoning through the faster relation retrieval of compact qualitative direction representations implemented on global equal latitude and longitude grids (ELLGs) and the ELLG-based quantitative calculations. The proposed method has been verified by two real-world datasets and proven to be efficient and accurate when locating POIs in large POI datasets from descriptions. Numéro de notice : A2022-177 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12838 Date de publication en ligne : 06/09/2021 En ligne : https://doi.org/10.1111/tgis.12838 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99834
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 129 - 154[article]Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)
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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)
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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)
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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)
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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)
PermalinkSynergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])
Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)
PermalinkAutomatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)
PermalinkVariable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)
Permalink3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)
PermalinkAdaptation d'un algorithme SLAM pour la vision panoramique multi-expositions dans des scènes à haute gamme dynamique / Eva Goichon (2022)
PermalinkPermalinkAn extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)
PermalinkPermalinkAnalysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
PermalinkPermalinkApport de l’intelligence artificielle au domaine des villes intelligentes : application à l’assistance des déplacements des personnes à mobilité réduite / Nathan Aky (2022)
PermalinkApprentissage profond pour l'imagerie SAR : du débruitage à l'interprétation de scène / Emanuele Dalsasso (2022)
PermalinkApprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)
PermalinkPermalinkATONTE: towards a new methodology for seed ontology development from texts and experts / Helen Mair Rawsthorne (2022)
PermalinkAutomatic identification of addresses: A systematic literature review / Paula Cruz in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
PermalinkA benchmark of named entity recognition approaches in historical documents : application to 19th century French directories / Nathalie Abadie (2022)
PermalinkBuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation / Minh On Vu Ngoc (2022)
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