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STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1559 - 1577 Titre : STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Zhenhong Du, Auteur ; Yuhua Gu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1559 - 1577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification barycentrique [Termes IGN] données spatiotemporelles [Termes IGN] exploration de données [Termes IGN] exploration de données géographiques [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] origine - destination [Termes IGN] Pékin (Chine) [Termes IGN] taxi Résumé : (Auteur) Clustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York. Numéro de notice : A2020-768 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12662 Date de publication en ligne : 19/07/2020 En ligne : https://doi.org/10.1111/tgis.12662 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96660 [article]

A hydrological sensor web ontology based on the SSN ontology: A case study for a flood / Chao Wang in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 1 (January 2018) Titre : A hydrological sensor web ontology based on the SSN ontology: A case study for a flood Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Nengcheng Chen, Auteur ; Wei Wang, Auteur ; Zeqiang Chen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classe d'objets [Termes IGN] données spatiotemporelles [Termes IGN] hydrographie [Termes IGN] modèle d'ontologie [Termes IGN] ontologie [Termes IGN] raisonnement sémantique [Termes IGN] réseau de capteurs Résumé : (Auteur) Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of semantics. In this paper, a hydrological sensor web ontology based on SSN ontology is proposed to describe the heterogeneous hydrological sensor web resources by importing the time and space ontology, instantiating the hydrological classes, and establishing reasoning rules. This work has been validated by semantic querying and knowledge acquiring experiments. The results demonstrate the feasibility and effectiveness of the proposed ontology and its potential to grow into a more comprehensive ontology for hydrological monitoring collaboratively. In addition, this method of ontology modeling is generally applicable to other applications and domains. Numéro de notice : A2018-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7010002 En ligne : https://doi.org/10.3390/ijgi7010002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89261 [article]

Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers / Zhengjia Zhang in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 39 (July 2015) . - pp 49 - 55 Titre : Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers Type de document : Article/Communication Auteurs : Zhengjia Zhang, Auteur ; Chao Wang, Auteur ; Yixian Tang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 49 - 55 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Chine [Termes IGN] effondrement de terrain [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] mine de charbon [Termes IGN] subsidence [Termes IGN] surveillance géologique Résumé : (auteur) In coal mining areas, ground subsidence persistently happens, which produces serious environmental issues and affects the development of cities. To monitor the ground deformation due to coal mining, a modified time-series InSAR technique combining persistent scatterers (PSs) and distributed scatterers (DSs) is presented in this paper. In particular, DSs are efficiently identified using classified information and statistical characteristics. Furthermore, a two-scale network is introduced into traditional PSI to deal with PSs and DSs in a multi-layer framework by taking the advantage of the robust of PSs and the widely distribution of DSs. The proposed method is performed to investigate the subsidence of Huainan City, Anhui province (China), during 2012–2013 using 14 scenes of Radarsat-2 images. Experimental results show that the proposed method can ease the estimation complexity and significantly increase the spatial density of measurement points, which can provide more detailed deformation information. Result shows that there are obvious subsidence areas detected in the test site with subsidence velocity larger than 5 cm/year. The proposed method brings practical applications for non-urban area deformation monitoring. Numéro de notice : A2015-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2015.02.007 En ligne : http://www.sciencedirect.com/science/article/pii/S0303243415000392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76476 [article]