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Spatiotemporal accuracy evaluation and errors analysis of global VTEC maps using a simulation technique / Jian Lin in GPS solutions, vol 27 n° 1 (January 2023)
[article]
Titre : Spatiotemporal accuracy evaluation and errors analysis of global VTEC maps using a simulation technique Type de document : Article/Communication Auteurs : Jian Lin, Auteur ; Xinxing Li, Auteur ; Shenfeng Gu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] données GPS
[Termes IGN] harmonique sphérique
[Termes IGN] modèle cartographique
[Termes IGN] modèle ionosphérique
[Termes IGN] phase
[Termes IGN] rayonnement solaire
[Termes IGN] simulation
[Termes IGN] station GPS
[Termes IGN] teneur verticale totale en électronsRésumé : (auteur) The computation of vertical total electron content (VTEC) maps has become an important issue gradually for the international GNSS service. Given the current literature reports, little research is involved in the quantitative analysis of each error of the VTEC map and the spatiotemporal characteristic of global VTEC accuracy. Based on the single layer model and sphere harmonic function, we propose an approach using simulated GPS data to comprehensively verify the accuracy of the VTEC map. The spatiotemporal characteristic of global VTEC accuracy and the errors induced by different processing steps, i.e., carrier phase to code leveling, mapping function (MF), DCB estimation and coefficient fitting, are analyzed and discussed in detail. In addition, the effect of solar activity on the accuracy of the global VTEC map, MF and DCB estimation has been discussed. The results suggest: First, it is found that the MF error at sunrise is more significant than that at sunset, and this important characteristic can be proven based on the analysis of theory and ionospheric radio occultation and VTEC measurements; second, the MF is the most significant error source in the VTEC processing for regions with dense and homogeneous distributed GPS stations, e.g., North America and Europe. The VTEC accuracy in these regions can be improved by 100% with the satellite elevation cutoff angle increasing from 12° to 30°; finally, compared with the global VTEC accuracy using 350 GPS stations observations, the accuracy is improved by 306% based on the double GPS stations with even distribution. Numéro de notice : A2023-002 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-022-01343-y Date de publication en ligne : 13/10/2022 En ligne : https://doi.org/10.1007/s10291-022-01343-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101871
in GPS solutions > vol 27 n° 1 (January 2023) . - n° 6[article]Understanding public perspectives on fracking in the United States using social media big data / Xi Gong in Annals of GIS, vol 29 n° 1 (January 2023)
[article]
Titre : Understanding public perspectives on fracking in the United States using social media big data Type de document : Article/Communication Auteurs : Xi Gong, Auteur ; Yujian Lu, Auteur ; Daniel Beene, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 21 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] analyse socio-économique
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données massives
[Termes IGN] enquête sociologique
[Termes IGN] Etats-Unis
[Termes IGN] fracturation
[Termes IGN] hétérogénéité spatiale
[Termes IGN] régression géographiquement pondérée
[Termes IGN] TwitterRésumé : (auteur) People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics. Numéro de notice : A2023-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2022.2121856 Date de publication en ligne : 10/09/2022 En ligne : https://doi.org/10.1080/19475683.2022.2121856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102862
in Annals of GIS > vol 29 n° 1 (January 2023) . - pp 21 - 35[article]Automatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)
[article]
Titre : Automatic registration of point cloud and panoramic images in urban scenes based on pole matching Type de document : Article/Communication Auteurs : Yuan Wang, Auteur ; Yuhao Li, Auteur ; Yiping Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103083 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de formes
[Termes IGN] chevauchement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image panoramique
[Termes IGN] image virtuelle
[Termes IGN] optimisation par essaim de particules
[Termes IGN] points registration
[Termes IGN] recalage d'image
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] zone tamponRésumé : (auteur) Given the initial calibration of multiple sensors, the fine registration between Mobile Laser Scanning (MLS) point clouds and panoramic images is still challenging due to the unforeseen movement and temporal misalignment while collecting. To tackle this issue, we proposed a novel automatic method to register the panoramic images and MLS point clouds based on the matching of pole objects. Firstly, 2D pole instances in the panoramic images are extracted by a semantic segmentation network and then optimized. Secondly, every corresponding frustum point cloud of each pole instance is obtained by a shape-adaptive buffer region in the panoramic image, and the 3D pole object is extracted via a combination of slicing, clustering, and connected domain analysis, then all 3D pole objects are fused. Finally, 2D and 3D pole objects are re-projected onto virtual images respectively, and then fine 2D-3D correspondences are collected through maximizing pole overlapping area by Particle Swarm Optimization (PSO). The accurate extrinsic orientation parameters are acquired by the Efficient Perspective-N-Point (EPnP). The experiments indicate that the proposed method performs effectively on two challenging urban scenes with an average registration error of 2.01 pixels (with RMSE 0.88) and 2.35 pixels (with RMSE 1.03), respectively. Numéro de notice : A2022-827 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103083 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103083 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102011
in International journal of applied Earth observation and geoinformation > vol 115 (December 2022) . - n° 103083[article]Fast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth / Fang Ouyang in Journal of geodesy, vol 96 n° 12 (December 2022)
[article]
Titre : Fast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth Type de document : Article/Communication Auteurs : Fang Ouyang, Auteur ; Long-wei Chen, Auteur ; Zhi-gang Shao, Auteur Année de publication : 2022 Article en page(s) : n° 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] champ de gravitation
[Termes IGN] coordonnées sphériques
[Termes IGN] discrétisation
[Termes IGN] intégrale de Newton
[Termes IGN] inversion
[Termes IGN] quadrature
[Termes IGN] tesseroid
[Termes IGN] transformation rapide de FourierRésumé : (auteur) Fast and accurate calculation of gravitational effects on a regional or global scale with complex density environment is a critical issue in gravitational forward modelling. Most existing significant developments with tessroid-based modelling are limited to homogeneous density models or polynomial ones of a limited order. Moreover, the total gravitational effects of tesseroids are often calculated by pure summation in these methods, which makes the calculation extremely time-consuming. A new efficient and accurate method based on tesseroids with a polynomial density up to an arbitrary order in depth is developed for 3D large-scale gravitational forward modelling. The method divides the source region into a number of tesseroids, and the density in each tesseroid is assumed to be a polynomial function of arbitrary degree. To guarantee the computational accuracy and efficiency, two key points are involved: (1) the volume Newton’s integral is decomposed into a one-dimensional integral with a polynomial density in the radial direction, for which a simple analytical recursive formula is derived for efficient calculation, and a surface integral over the horizontal directions evaluated by the Gauss–Legendre quadrature (GLQ) combined with a 2D adaptive discretization strategy; (2) a fast and flexible discrete convolution algorithm based on 1D fast Fourier transform (FFT) and a general Toepritz form of weight coefficient matrices is adopted in the longitudinal dimension to speed up the computation of the cumulative contributions from all tesseroids. Numerical examples show that the gravitational fields predicted by the new method have a good agreement with the corresponding analytical solutions for spherical shell models with both polynomial and non-polynomial density variations in depth. Compared with the 3D GLQ methods, the new algorithm is computationally more accurate and efficient. The calculation time is significantly reduced by 3 orders of magnitude as compared with the traditional 3D GLQ methods. Application of the new algorithm in the global crustal CRUST1.0 model further verifies its reliability and practicability in real cases. The proposed method will provide a powerful numerical tool for large-scale gravity modelling and also an efficient forward engine for inversion and continuation problems. Numéro de notice : A2022-896 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01688-9 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01688-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102248
in Journal of geodesy > vol 96 n° 12 (December 2022) . - n° 97[article]Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] ArcGIS
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] classification par arbre de décision
[Termes IGN] colinéarité
[Termes IGN] estimation bayesienne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] TéhéranRésumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292[article]Reconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)PermalinkSea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)PermalinkA whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkAn improved optimization model for crowd evacuation considering individual exit choice preference / Fei Gao in Transactions in GIS, vol 26 n° 7 (November 2022)PermalinkA fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation / Xiaoguo Guan in Survey review, vol 54 n° 387 (November 2022)PermalinkGeographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)PermalinkMulti-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)PermalinkThe employment of quasi-hexagonal grids in spherical harmonic analysis and synthesis for the earth's gravity field / Xingxing Li in Journal of geodesy, vol 96 n° 11 (November 2022)PermalinkTidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)PermalinkFlash-flood hazard susceptibility mapping in Kangsabati River Basin, India / Rabin Chakrabortty in Geocarto international, vol 37 n° 23 ([15/10/2022])Permalink