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1996–2017 GPS position time series, velocities and quality measures for the CORS Network / Jarir Saleh in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 105 - 115 Titre : 1996–2017 GPS position time series, velocities and quality measures for the CORS Network Type de document : Article/Communication Auteurs : Jarir Saleh, Auteur ; Sungpil Yoon, Auteur ; Kevin Choi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 105 - 115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] Continuously Operating Reference Station network [Termes IGN] données GPS [Termes IGN] données localisées des bénévoles [Termes IGN] Etats-Unis [Termes IGN] positionnement par GPS [Termes IGN] qualité des données [Termes IGN] série temporelle [Termes IGN] station de référence Résumé : (Auteur) The CORS network is a volunteer-based network of Global Positioning System reference stations located mainly in the US and its territories. We discuss the most recent comprehensive reprocessing of all GPS data collected via this network since 1996. Daily data for GPS weeks 834 through 1933 were reprocessed leading to epoch 2010.0 coordinates and velocities of 3049 stations aligned to IGS14. The updated realization of the US National Spatial Reference System derived in this work has been in use since late 2019. As a validation of the results, the derived velocity field is compared to several other solutions and to three regional geophysical and geodetic velocity models. These comparisons uncovered unstable stations which move differently than the regional kinematics around them. Once these are ignored, we estimate the horizontal and vertical stability of this updated realization to be better than ∼0.3 and ∼0.6 mm/year, respectively. We use the position residuals and estimated uncertainties from this reprocessing to derive long-term stability measures for all active stations serving longer than 3 years. These measures exposed ∼60 CORS with the poorest long-term stability, which have been consequently excluded from serving as mapping control. Numéro de notice : A2021-320 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0041 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.1515/jag-2020-0041 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97476 [article]

Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 117 - 133 Titre : Machine learning and geodesy: A survey Type de document : Article/Communication Auteurs : Jemil Butt, Auteur ; Andreas Wieser, Auteur ; Zan Gojcic, Auteur ; Caifa Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 117 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse de données [Termes IGN] apprentissage automatique [Termes IGN] données géodésiques [Termes IGN] espace de Hilbert [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise. Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy. Numéro de notice : A2021-321 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0043 Date de publication en ligne : 20/02/2021 En ligne : https://doi.org/10.1515/jag-2020-0043 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97478 [article]

Identification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust / Kamil Kowalczyk in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 153 - 167 Titre : Identification of common points in hybrid geodetic networks to determine vertical movements of the Earth’s crust Type de document : Article/Communication Auteurs : Kamil Kowalczyk, Auteur ; Anna Maria Kowalczyk, Auteur ; Jacek Rapinski, Auteur Année de publication : 2021 Article en page(s) : pp 153 - 167 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] déformation verticale de la croute terrestre [Termes IGN] géodynamique [Termes IGN] noeud [Termes IGN] réseau géodésique [Termes IGN] réseau hybride [Termes IGN] station GNSS Résumé : (Auteur) Simultaneous use of data repeated levelling measurements and continuous GNSS observations allows increasing the spatial resolution of geodynamics models. For this purpose, it is necessary to create a single network, a so-called hybrid network. This paper aims at examining the possibility of using scale-free network theory to determine the most relevant common points in hybrid networks using the distance criterion. Used on European network points: UELN (United European Levelling Network) and EPN (European Permanent GPS Network) and the regional network. In the hybrid network (UELN + EPN), 18 pseudo-nodal points with the highest number of links were identified. The accepted distance criterion shows that about 90 % of the EPN points can be used as common points. The application of the scale-free network theory allows determining the significance of points in a hybrid network. Numéro de notice : A2021-322 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2021-0002 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.1515/jag-2021-0002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97479 [article]