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Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (May 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (May 2020) . - pp 277 - 288 Titre : Delineating and modeling activity space using geotagged social media data Type de document : Article/Communication Auteurs : Lingqian Hu, Auteur ; Zhenhong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2020 Article en page(s) : pp 277 - 288 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distance [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données socio-économiques [Termes IGN] logement [Termes IGN] loisir [Termes IGN] Los Angeles [Termes IGN] quartier [Termes IGN] réseau social [Termes IGN] sport [Termes IGN] Twitter [Termes IGN] voisinage (topologie) [Termes IGN] zone urbaine Résumé : (auteur) It has become increasingly important in spatial equity studies to understand activity spaces – where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users’ home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research. Numéro de notice : A2020-135 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1705187 date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/15230406.2019.1705187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94843 [article]

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Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 38 - 53 Titre : Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features Type de document : Article/Communication Auteurs : Peijun Du, Auteur ; Alim Samat, Auteur ; Björn Waske, Auteur ; Sicong Liu, Auteur ; Zhenhong Li, Auteur Année de publication : 2015 Article en page(s) : pp 38 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par forêts aléatoires [Termes IGN] données polarimétriques [Termes IGN] image Radarsat [Termes IGN] polarimétrie radar [Termes IGN] Rotation Forest classification [Termes IGN] texture d'image Résumé : (auteur) Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. Numéro de notice : A2015-706 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://www.sciencedirect.com/science/article/pii/S0924271615000611 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78342 [article]

Using small baseline Interferometric SAR to map nonlinear ground motion: a case study in Northern Tibet / Zhenhong Li in Journal of applied geodesy, vol 3 n° 3 (August 2009)  

Public [article]  inJournal of applied geodesy > vol 3 n° 3 (August 2009) . - pp 163 - 170 Titre : Using small baseline Interferometric SAR to map nonlinear ground motion: a case study in Northern Tibet Type de document : Article/Communication Auteurs : Zhenhong Li, Auteur ; Yanxiong Liu, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 163 - 170 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] déformation de la croute terrestre [Termes IGN] image Envisat-ASAR [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] mouvement de terrain [Termes IGN] Tibet Résumé : (auteur) With its global coverage and all-weather imaging capability, Interferometric SAR (InSAR) has been revolutionizing our ability to image the Earth's surface and the evolution of its shape over time. In turn, this has led to many new insights into geophysical and engineering processes, such as volcanoes, earthquakes, landslides and mining activity. In this study, we used an advanced InSAR time series technique to map ground motion of an area in Northern Tibet using ENVISAT images acquired between 2003 and 2007. In order to minimise the effects of baseline decorrelation, a subset of possible pairs having a perpendicular baseline (i.e. orbital separation) of less than 400 m was chosen for the InSAR time series analysis. The time series results reveal an ‘unexpected’ nonlinear ground motion: the area of interest was relatively stable during the period from 2003 to the middle of 2004, whilst it has exhibited a nearly linear uplift of about 8 cm since the middle of 2004. Examination of high-resolution ALOS PRISM images shows that the uplift signal occurred over the Huatugou oil field and is most likely caused by water injection. This study highlights the potential of InSAR as an early detection tool of surface deformations. Numéro de notice : A2009-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/JAG.2009.017 date de publication en ligne : 18/08/2009 En ligne : https://doi.org/10.1515/JAG.2009.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75409 [article]

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