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Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation / Boxi Shen in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 683 Titre : Unfolding spatial-temporal patterns of taxi trip based on an improved network kernel density estimation Type de document : Article/Communication Auteurs : Boxi Shen, Auteur ; Xiang Xu, Auteur ; Jun Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 683 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] appariement de cartes [Termes IGN] estimation par noyau [Termes IGN] mobilité urbaine [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modèle conceptuel de flux [Termes IGN] Shenzhen [Termes IGN] taxi [Termes IGN] trafic routier [Termes IGN] trafic urbain [Termes IGN] trajet (mobilité) Résumé : (auteur) Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes. Numéro de notice : A2020-730 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110683 Date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110683 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96337 [article]

Multiple morphological component analysis based decomposition for remote sensing image classification / Xiang Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 3083 - 3102 Titre : Multiple morphological component analysis based decomposition for remote sensing image classification Type de document : Article/Communication Auteurs : Xiang Xu, Auteur ; Jun Li, Auteur ; Xin Huang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3083 - 3102 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] décomposition d'image [Termes IGN] image multi sources [Termes IGN] morphologie mathématique Résumé : (Auteur) Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. Numéro de notice : A2016-848 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2511197 En ligne : https://doi.org/10.1109/TGRS.2015.2511197 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82929 [article]