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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)  

Public [article]  inTransactions in GIS > vol 26 n° 2 (April 2022) . - pp 735 - 754 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 [article]

Pattern-mining approach for conflating crowdsourcing road networks with POIs / Bisheng Yang in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 5 (May 2015) . - pp 786 - 805 Titre : Pattern-mining approach for conflating crowdsourcing road networks with POIs Type de document : Article/Communication Auteurs : Bisheng Yang, Auteur ; Yunfei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 786 - 805 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] acquisition de données [Termes IGN] appariement de données localisées [Termes IGN] conflation [Termes IGN] données localisées [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] exploration de données [Termes IGN] graphe [Termes IGN] point d'intérêt [Termes IGN] précision du positionnement [Termes IGN] précision géométrique (imagerie) [Termes IGN] précision sémantique [Termes IGN] qualité des données [Termes IGN] réseau routier [Termes IGN] squelettisation Résumé : (Auteur) Crowdsourcing geospatial data mainly collected by public citizens have brought about a profound transformation on data acquisition and utilization. However, the unpredictable positional accuracies, unstructured semantic descriptions, and invalid spatial relations occur to crowdsourcing geospatial data, causing difficulties for conflating heterogeneous data sets collected by different professional agencies or volunteers. We thus propose a novel pattern-mining approach to conflate crowdsourcing road networks with points of interest (POIs) geometrically and semantically. The proposed method mines the geometric patterns between road networks and POIs respectively and generates the pattern-related skeleton graphs for them. Then, corresponding points are determined between the two skeleton graphs to align POIs and road networks geometrically, and the road-related semantic data between the associated POIs and the road segments are compared to check the data quality of POIs and infer the road names of the road segments. Experimental results show the advantages of our proposed method, demonstrating a functional and promising solution for enriching POIs and road network geometrically and semantically. Numéro de notice : A2015-593 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.997238 En ligne : https://doi.org/10.1080/13658816.2014.997238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77883 [article]