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Une généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2022)
Titre : Une généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue Type de document : Article/Communication Auteurs : Philippe Brion, Auteur ; Olivier Bouriaud , Auteur ; Guillaume Chauvet, Auteur Editeur : Rennes : Université de Rennes 1 Année de publication : 2022 Projets : 1-Pas de projet / Conférence : JMS 2022, 14es Journées de méthodologie statistique de l’Insee Paris France Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] échantillonnage
[Termes IGN] estimateur
[Termes IGN] estimation statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] méthode de partage des poids
[Termes IGN] quantité continue
[Termes IGN] stratification
[Termes IGN] variance
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) La définition de l’unité statistique utilisée dans les enquêtes statistiques est une question difficile : les différents ”univers” enquêtés n’ont pas nécessairement une base de sondage directement utilisable, et il arrive que l’on utilise des unités à échantillonner d’une nature différente de celle des unités observées. La production d’estimations statistiques pose alors des problèmes méthodologiques complexes, qui peuvent être traités en utilisant la méthode dite du partage des poids, formalisée par Deville et Lavallée (2006). Cette méthode est basée sur les liens existant entre les deux populations : population échantillonnée et population observée. Cependant, les deux populations considérées dans cette approche sont des populations discrètes. Pour certains domaines d’étude, en particulier liés à des aspects environnementaux, la population échantillonnée est une population continue : c’est par exemple le cas des inventaires forestiers pour lesquels, fréquemment, les arbres enquêtés sont ceux situés sur des placettes dont les centres sont des points tirés de manière aléatoire dans une zone donnée. La production d’estimations statistiques à partir de l’échantillon d’arbres enquêtés pose alors des difficultés de méthode, ainsi que les calculs de variance associés. L’objet de ce papier est de procéder à une généralisation de la méthode de partage des poids au cas continu (population échantillonnée) – discret (population enquêtée), à partir de la formalisation proposée par Cordy en 1993 sur l’extension de l’estimateur de Horvitz-Thompson au tirage de points réalisé dans un univers continu. Numéro de notice : C2022-001 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : http://www.jms-insee.fr/2022/S15_3_ACTE_BOURIAUD_BRION_JMS2022.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103348 Documents numériques
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Une généralisation de la méthode de partage ... - pdf éditeurAdobe Acrobat PDF Generating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)
[article]
Titre : Generating GPS decoupled clock products for precise point positioning with ambiguity resolution Type de document : Article/Communication Auteurs : Shuai Liu, Auteur ; Yunbin Yan, Auteur Année de publication : 2022 Article en page(s) : n° 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] erreur de phase
[Termes IGN] horloge du récepteur
[Termes IGN] modèle stochastique
[Termes IGN] onde porteuse
[Termes IGN] positionnement cinématique
[Termes IGN] positionnement ponctuel précis
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Carrier-phase integer ambiguity resolution (AR) is the key to improving the positioning accuracy of precise point positioning (PPP). However, in theory, the integer property of ambiguities in PPP are destroyed due to the absorption of phase biases. In this study, we analyzed a set of clock products consisting of a code clock, phase clock and wide-lane (WL) bias based on the decoupled clock (DCK) model, to facilitate PPP AR. The determination of the datum of the receiver clock as well as ambiguity were analyzed in detail to arrive at ways to eliminate rank deficiency. To fix ambiguity at the server end, we propose an approach by rounding directly with “fixing solution” (FS) and “partial ambiguity hold” (PAH) strategies, to increase the fixing rate and avoid the biased solution resulting from ambiguity datum loss. With respect to the International GNSS Service (IGS) legacy clocks, the mean standard deviations (STDs) of the phase clock and code clock were about 0.02 and 1.05 ns respectively, while the WL bias was about 0.12 cycles. Additionally, the convergence speed and stability of the decoupled phase clock are significantly improved compared with the conventional PPP model. Experiments on PPP positioning performance were conducted using 1 week of GPS data from more than 100 stations, considering the IGS weekly solutions as a benchmark. The ambiguity-fixed PPP with decoupled clocks had almost the same accuracy as the integer-recovered clock model, but the average accuracy improvements compared with the conventional PPP model in the east, north, and up components were 59.2, 32.4, and 20.3%, respectively, in the static mode, and approximately 38.0, 26.2, and 19.2% in the kinematic mode. These results demonstrate that users can achieve ambiguity-fixed solutions and obtain high-precision positioning coordinates with our decoupled clock products. Numéro de notice : A2022-093 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01593-7 En ligne : https://doi.org/10.1007/s00190-021-01593-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99517
in Journal of geodesy > vol 96 n° 1 (January 2022) . - n° 6[article]Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
[article]
Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]
Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] chaîne de traitement
[Termes IGN] échantillonnage
[Termes IGN] modèle stochastique
[Termes IGN] ontologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] science des données
[Termes IGN] série temporelleRésumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture
2- Advanced techniques and methods
3- Organizing and Exploiting MetadataNuméro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469 A method for precisely predicting satellite clock bias based on robust fitting of ARMA models / Guochao Zhang in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : A method for precisely predicting satellite clock bias based on robust fitting of ARMA models Type de document : Article/Communication Auteurs : Guochao Zhang, Auteur ; Songhui Han, Auteur ; Jun Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] décalage d'horloge
[Termes IGN] erreur systématique interfréquence d'horloge
[Termes IGN] estimation bayesienne
[Termes IGN] international GPS service for geodynamics
[Termes IGN] série temporelle
[Termes IGN] statistique mathématique
[Termes IGN] valeur aberranteRésumé : (auteur) The precise satellite clock bias prediction is critical in improving the positioning, navigation and timing (PNT) service capabilities of the global navigation satellite system (GNSS). Due to the influence of satellite signal path and the observation environment, the satellite clock bias data usually contain outliers that heavily affect the accuracy of satellite clock bias prediction. Based on the time series ARMA model and Bayes statistical theory, we propose a method to precisely predict satellite clock bias and detect outliers in the historical sequence of satellite clock bias. At first, considering the effects of an additive outlier (AO) and innovative outlier (IO), a labeling model for robustly fitting the time series ARMA model and detecting AOs and IOs simultaneously is constructed based on the labeling method of classification variables. Second, the Bayes method for robustly fitting time series ARMA model is proposed based on the Bayes statistical theory. Furthermore, it develops an algorithm to precisely predict satellite clock bias using the Bayes method for robustly fitting the time series ARMA model mentioned above. Finally, in order to illustrate the performance of the method for precisely predicting satellite clock bias that we presented, three examples are designed based on the real GPS data come from the IGS official website, and the prediction results of the method are compared with that of original ARMA model (oARMA), quadratic polynomial model (QP) and gray model (GM). It is found that the method can precisely predict the satellite clock bias as well as accurately detect the outliers in the historical sequence. Numéro de notice : A2022-002 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01182-3 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01182-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98827
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 3[article]Photogrammetric 3D mobile mapping of rail tracks / Philipp Glira in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)PermalinkProceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. 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Forrester in European Journal of Forest Research, vol 140 n° 4 (August 2021)PermalinkStructure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkDeformation analysis of a reference wall towards the uncertainty investigation of terrestrial laser scanners / Berit Schmitz in Journal of applied geodesy, vol 15 n° 3 (July 2021)PermalinkIonospheric irregularity layer height and thickness estimation with a GNSS receiver array / Seebany Datta-Barua in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkRole of maximum entropy and citizen science to study habitat suitability of jacobin cuckoo in different climate change scenarios / Priyinka Singh in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkVectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkA framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)PermalinkHorvitz-Thompson–like estimation with distance-based detection probabilities for circular plot sampling of forests / Kasper Kansanen in Biometrics, vol 77 n° 2 (June 2021)PermalinkMapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkRobust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds / Reza Maalek in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkSpatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkUncertainty management for robust probabilistic change detection from multi-temporal Geoeye-1 imagery / Mahmoud Salah in Applied geomatics, vol 13 n° 2 (June 2021)PermalinkA Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])PermalinkAn improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkBias in least-squares adjustment of implicit functional models / Michael Lösler in Survey review, Vol 53 n° 378 (May 2021)PermalinkIncreasing efficiency of the robust deformation analysis methods using genetic algorithm and generalised particle swarm optimisation / Mehmed Batilović in Survey review, Vol 53 n° 378 (May 2021)PermalinkIntegrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)PermalinkA new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)PermalinkObservable quality assessment of broadband very long baseline interferometry system / Ming H. Xu in Journal of geodesy, vol 95 n° 5 (May 2021)PermalinkUnderstanding collective human movement dynamics during large-scale events using big geosocial data analytics / Junchuan Fan in Computers, Environment and Urban Systems, vol 87 (May 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkImpact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences / Jiang Guo in GPS solutions, vol 25 n° 2 (April 2021)PermalinkIntegrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A / Pierre Bosser in Earth System Science Data, vol 13 n° 4 (April 2021)PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)PermalinkSpectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkAnalyse et consolidation des résultats sur les estimations de superficie du couvert forestier et de ses changements entre 2000 et 2016 en république du Congo / Suspense Averti Ifo in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkGeographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships / Sensen Wu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkGridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 2021)PermalinkIntegrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)PermalinkLandslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkModélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase / Camille Parra in XYZ, n° 166 (mars 2021)PermalinkSpace-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)PermalinkSusceptibilité aux glissements de terrain dans la ville d’Al Hoceima et sa périphérie : application de la méthode de la théorie de l’évidence / Taoufik Byou in Geomatica, vol 75 n° 1 (Mars 2021)PermalinkUrban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)PermalinkAn anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkCrowdsourcing without data bias: Building a quality assurance system for air pollution symptom mapping / Marta Samulowska in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)PermalinkA highly adaptable method for GNSS cycle slip detection and repair based on Kalman filter / Xianwen Yu in Survey review, Vol 53 n° 377 (February 2021)PermalinkIWV retrieval from ground GNSS receivers during NAWDEX / Pierre Bosser in Advances in geosciences, vol 55 ([01/02/2021])PermalinkLand cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)PermalinkOptimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale / Linling Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkA practical method for calculating reliable integer float estimator in GNSS precise positioning / Xianwen Yu in Survey review, Vol 53 n° 377 (February 2021)PermalinkStochastic model reliability in GNSS baseline solution / Aviram Borko in Journal of geodesy, vol 95 n° 2 (February 2021)PermalinkStudy of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkUsing automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)PermalinkPermalinkAleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkAn efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)PermalinkAre there detectable common aperiodic displacements at ITRF co-location sites? / Maylis Teyssendier de la Serve (2021)PermalinkPermalinkBenefits from a multi-receiver architecture for GNSS RTK positioning and attitude determination / Xiao Hu (2021)PermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkConvex hull: another perspective about model predictions and map derivatives from remote sensing data / Jean-Pierre Renaud (2021)PermalinkCorrecting misclassification errors in crowdsourced ecological data: A Bayesian perspective / Edgar Santos-Fernandez in Journal of the Royal Statistical Society: Series C Applied Statistics, vol 70 n° 1 (January 2021)PermalinkPermalinkDéveloppement d'un modèle de macro-dynamique forestière pour simuler la dynamique des forêts françaises dans un contexte non-stationnaire / Timothée Audinot (2021)Permalink