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Navigation network derivation for QR code-based indoor pedestrian path planning / Jinjin Yan in Transactions in GIS, vol 26 n° 3 (May 2022)
[article]
Titre : Navigation network derivation for QR code-based indoor pedestrian path planning Type de document : Article/Communication Auteurs : Jinjin Yan, Auteur ; Jinwoo Lee, Auteur ; Sisi Zlatanova, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1240 - 1255 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] batiment commercial
[Termes IGN] bâtiment public
[Termes IGN] navigation pédestre
[Termes IGN] noeud
[Termes IGN] point d'intérêt
[Termes IGN] positionnement en intérieur
[Termes IGN] QR code
[Termes IGN] scène intérieure
[Termes IGN] trajet (mobilité)Résumé : (auteur) With the development of cities, the indoor structures of contemporary public or commercial buildings are becoming increasingly complex. Accordingly, the need for indoor navigation has arisen. Among the indoor positioning technologies, quick response (QR) code, a low-cost, easily deployable, flexible, and efficient approach, has been used for indoor positioning and navigation purposes. A navigation network (model) is a precondition for pedestrian navigation path planning. However, no thorough research has been completed to investigate the relationship between navigation networks and locations of QR codes, which may cause ambiguities when deciding the closest node from the network that should be used for path computation. Specifically, QR codes are generally placed according to preferences or certain specifications whereas current agreed navigation network derivation approaches do not consider that. This article presents a navigation network derivation approach to address the issue by integrating QR code locations as nodes in navigation networks. The present approach is demonstrated in a shopping mall case. The results show that the approach can overcome the above-mentioned issue for indoor pedestrian path planning based on the QR code localization. Numéro de notice : A2022-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12912 Date de publication en ligne : 10/04/2022 En ligne : https://doi.org/10.1111/tgis.12912 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100823
in Transactions in GIS > vol 26 n° 3 (May 2022) . - pp 1240 - 1255[article]Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey / Faruk Yildirim in Geocarto international, vol 37 n° 8 ([01/05/2022])
[article]
Titre : Production of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey Type de document : Article/Communication Auteurs : Faruk Yildirim, Auteur ; Fatih Kadi, Auteur Année de publication : 2022 Article en page(s) : pp 2175 - 2197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] carte forestière
[Termes IGN] chemin forestier
[Termes IGN] interface graphique
[Termes IGN] Matlab
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] recherche du chemin optimal, algorithme de
[Termes IGN] TurquieRésumé : (auteur) Forest roads are a basic necessity in forestry policies and should be planned by considering many factors. This study aims to generate optimum forest road routes and to compare them with current forest roads. First, FRNSM has been produced according to AHP, using nine factors for the study area. Then, risk statuses of the current forest roads are examined. According to results, 35% of the total forest road has high risk. A MATLAB-GUI based an application using optimal path algorithm developed for the second stage of the study has been produced. Using this application, optimum forest road routes have been produced for 11 pilot areas selected from the region. Generated routes have been compared with current forest roads in the region. It has been observed that generated routes in all areas are more suitable than current forest roads in terms of total length and average risk of suitability. Numéro de notice : A2022-504 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1818852 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1818852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101025
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2175 - 2197[article]A knowledge representation model based on the geographic spatiotemporal process / Kun Zheng in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
[article]
Titre : A knowledge representation model based on the geographic spatiotemporal process Type de document : Article/Communication Auteurs : Kun Zheng, Auteur ; Ming Hui Xie, Auteur ; Jin Biao Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 674 - 691 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] ontologie
[Termes IGN] raisonnement spatiotemporel
[Termes IGN] représentation des connaissances
[Termes IGN] représentation du changement
[Termes IGN] représentation géographique
[Termes IGN] réseau sémantiqueRésumé : (auteur) Knowledge graphs (KGs) represent entities and relations as computable networks, which is of great value for discovering hidden knowledge and patterns. Geographic KGs mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic spatiotemporal processes. By analyzing the spatiotemporal features and evolution of geographic elements, this study presents the geographic evolutionary knowledge graph (GEKG). Its representation model has five core elements: time, geographic event (geo-event), geographic entity (geo-entity), activity and property, and defines six relations: logical, semantic, evolutionary and temporal relation, participation and inclusion. It establishes a hierarchical cubical model structure and each temporal layer extends vertically and horizontally starting with the earliest geo-event. Vertical expansion refers to the connection between different kinds of element, such as the participation relation between geo-entities and geo-events. Horizontal expansion indicates the association between the same kinds of element, such as the semantic relation between geo-entities. For different layers, the spatiotemporal differences of elements produce the evolutionary relation. Finally, the comparison of GEKG with Yet Another Great Ontology (YAGO) and Geographic Knowledge Graph (GeoKG) shows that GEKG has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. Numéro de notice : A2022-255 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1962527 Date de publication en ligne : 05/08/2021 En ligne : https://doi.org/10.1080/13658816.2021.1962527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100228
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 674 - 691[article]Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)
[article]
Titre : Mining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction Type de document : Article/Communication Auteurs : Jincai Huang, Auteur ; Yunfei Zhang, Auteur ; Min Deng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 735 - 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Vedettes matières IGN] Géomatique
[Termes IGN] base de données routières
[Termes IGN] carrefour
[Termes IGN] carte routière
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données localisées des bénévoles
[Termes IGN] données routières
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] information sémantique
[Termes IGN] intégration de données
[Termes IGN] navigation automobile
[Termes IGN] vitesse
[Termes IGN] Wuhan (Chine)Résumé : (auteur) The road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory-based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide-and-conquer method to construct a spatial-semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo-tagged data). The proposed method divides road map construction into two sub-tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo-tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update. Numéro de notice : A2022-364 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12879 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1111/tgis.12879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100583
in Transactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754[article]MTLM: a multi-task learning model for travel time estimation / Saijun Xu in Geoinformatica, vol 26 n° 2 (April 2022)
[article]
Titre : MTLM: a multi-task learning model for travel time estimation Type de document : Article/Communication Auteurs : Saijun Xu, Auteur ; Ruoqian Zhang, Auteur ; Wanjun Cheng, Auteur ; Jiajie Xu, Auteur Année de publication : 2022 Article en page(s) : pp 379 - 395 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse coût-avantage
[Termes IGN] apprentissage automatique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] durée de trajet
[Termes IGN] modèle de simulation
[Termes IGN] transport collectif
[Termes IGN] transport intermodalRésumé : (auteur) Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods. Numéro de notice : A2022-325 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : https://doi.org/10.1007/s10707-020-00422-x Date de publication en ligne : 15/08/2020 En ligne : https://doi.org/10.1007/s10707-020-00422-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100488
in Geoinformatica > vol 26 n° 2 (April 2022) . - pp 379 - 395[article]Automated 3D reconstruction of LoD2 and LoD1 models for All 10 million buildings of the Netherlands / Ravi Peters in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkExploring the relationship between the 2D/3D architectural morphology and urban land surface temperature based on a boosted regression tree: A case study of Beijing, China / Zhen Li in Sustainable Cities and Society, vol 78 (March 2022)PermalinkIGN, changer d'échelle ! / Jean-Pierre Maillard in XYZ, n° 170 (mars 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkSuspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkAn integrated framework of global sensitivity analysis and calibration for spatially explicit agent-based models / Jeon-Young Kang in Transactions in GIS, vol 26 n° 1 (February 2022)PermalinkAssessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria / Aida Bensekhria in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)PermalinkGazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules / Xuke Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkQuickly 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)PermalinkRaw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)Permalink