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Error propagation for the Molodensky G1 term / Jack C. McCubbine in Journal of geodesy, vol 93 n°6 (June 2019)  

Public [article]  inJournal of geodesy > vol 93 n°6 (June 2019) . - pp 889 - 898 Titre : Error propagation for the Molodensky G1 term Type de document : Article/Communication Auteurs : Jack C. McCubbine, Auteur ; Will E. Featherstone, Auteur ; Nicholas J. Brown, Auteur Année de publication : 2019 Article en page(s) : pp 889 - 898 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] anomalie de pesanteur [Termes descripteurs IGN] Australie [Termes descripteurs IGN] géoïde gravimétrique [Termes descripteurs IGN] géoïde local [Termes descripteurs IGN] hauteur ellipsoïdale [Termes descripteurs IGN] incertitude de position [Termes descripteurs IGN] intégrale de Stokes [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] problème des valeurs limites [Termes descripteurs IGN] propagation d'erreur [Termes descripteurs IGN] quasi-géoïde [Termes descripteurs IGN] transformation de coordonnées Résumé : (auteur) Molodensky G terms are used in the computation of the quasigeoid. We derive error propagation formulas that take into account uncertainties in both the free air gravity anomaly and a digital elevation model. These are applied to generate G1 terms and their errors on a 1″ × 1″ grid over Australia. We use these to produce Molodensky gravity anomaly and accompanying uncertainty grids. These uncertainties have average value of 2 mGal with maximum of 54 mGal. We further calculate a gravimetric quasigeoid model by the remove–compute–restore technique. These Molodensky gravity anomaly uncertainties lead to quasigeoid uncertainties with a mean of 4 mm and maximum of 80 mm when propagated through a deterministically modified Stokes’s integral over an integration cap radius of 0.5°. Numéro de notice : A2019-351 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-1211-6 date de publication en ligne : 09/11/2018 En ligne : https://doi.org/10.1007/s00190-018-1211-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93395 [article]

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1837 - 1859 Titre : Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Sisi Zhao, Auteur ; Qing Wang, Auteur ; Wenzhong Shi, Auteur Année de publication : 2018 Article en page(s) : pp 1837 - 1859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] propagation d'erreur Résumé : (Auteur) In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty. Numéro de notice : A2018-305 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1469136 date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1469136 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90448 [article]

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The effect of acquisition error and level of detail on the accuracy of spatial analyses / Filip Biljecki in Cartography and Geographic Information Science, Vol 45 n° 2 (March 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 2 (March 2018) . - pp 156 - 176 Titre : The effect of acquisition error and level of detail on the accuracy of spatial analyses Type de document : Article/Communication Auteurs : Filip Biljecki, Auteur ; Gerard B.M. Heuvelink, Auteur ; Hugo Ledoux, Auteur ; Jantien E. Stoter, Auteur Année de publication : 2018 Article en page(s) : pp 156 - 176 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] CityGML [Termes descripteurs IGN] erreur de positionnement [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] propagation d'erreur Résumé : (Auteur) There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. Numéro de notice : A2018-008 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2017.1279986 En ligne : https://doi.org/10.1080/15230406.2017.1279986 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88977 [article]

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The first Australian gravimetric quasigeoid model with location-specific uncertainty estimates / Will E. Featherstone in Journal of geodesy, vol 92 n° 2 (February 2018)  

Public [article]  inJournal of geodesy > vol 92 n° 2 (February 2018) . - pp 149 - 168 Titre : The first Australian gravimetric quasigeoid model with location-specific uncertainty estimates Type de document : Article/Communication Auteurs : Will E. Featherstone, Auteur ; Jack C. McCubbine, Auteur ; Nicholas J. Brown, Auteur ; S.J. Claessens, Auteur ; M. S. Filmer, Auteur ; J.F. Kirby, Auteur Année de publication : 2018 Article en page(s) : pp 149 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] géoïde gravimétrique [Termes descripteurs IGN] géoïde local [Termes descripteurs IGN] propagation d'erreur [Termes descripteurs IGN] quasi-géoïde Résumé : (Auteur) We describe the computation of the first Australian quasigeoid model to include error estimates as a function of location that have been propagated from uncertainties in the EGM2008 global model, land and altimeter-derived gravity anomalies and terrain corrections. The model has been extended to include Australia’s offshore territories and maritime boundaries using newer datasets comprising an additional ∼280,000 land gravity observations, a newer altimeter-derived marine gravity anomaly grid, and terrain corrections at 1′′×1′′ resolution. The error propagation uses a remove–restore approach, where the EGM2008 quasigeoid and gravity anomaly error grids are augmented by errors propagated through a modified Stokes integral from the errors in the altimeter gravity anomalies, land gravity observations and terrain corrections. The gravimetric quasigeoid errors (one sigma) are 50–60 mm across most of the Australian landmass, increasing to ∼100 mm in regions of steep horizontal gravity gradients or the mountains, and are commensurate with external estimates. Numéro de notice : A2018-059 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1053-7 En ligne : https://doi.org/10.1007/s00190-017-1053-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89392 [article]

Bayesian statistics and Monte Carlo methods / Karl Rudolf Koch in Journal of geodetic science, vol 8 n° 1 (January 2018)  

Public [article]  inJournal of geodetic science > vol 8 n° 1 (January 2018) . - pp 18 - 29 Titre : Bayesian statistics and Monte Carlo methods Type de document : Article/Communication Auteurs : Karl Rudolf Koch, Auteur Année de publication : 2018 Article en page(s) : pp 18 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] propagation d'erreur [Termes descripteurs IGN] théorème de Bayes [Termes descripteurs IGN] variable aléatoire [Termes descripteurs IGN] vecteur aléatoire multidimensionnel Résumé : (Auteur) The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes’ theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. The Monte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum. Numéro de notice : A2018-613 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2018-0003 date de publication en ligne : 02/03/2018 En ligne : https://doi.org/10.1515/jogs-2018-0003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92646 [article]

Level of detail in 3D city models / Filip Biljecki (2017)  

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Vision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)  

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A measure of average error variance of line features / Eryong Liu in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

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Propagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

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Qualité des données géographiques : à propos de la propagation des incertitudes / Gilles Troispoux in Signature, n° 59 (janvier 2016)

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Geometric accuracy analysis model of the ZiYuan-3 satellite without GCPs / Xinming Tang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)

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A novel negative abundance‐oriented hyperspectral unmixing algorithm / Rubén Marrero in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

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Analysis of star camera errors in GRACE data and their impact on monthly gravity field models / Pedro Inácio in Journal of geodesy, vol 89 n° 6 (June 2015)  

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In situ calibration of light sensors for long-term monitoring of vegetation / Hongxiao Jin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)

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Multi-view 3D circular target reconstruction with uncertainty analysis / Bahman Soheilian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3 (September 2014)    

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