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A new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello (2020)
Titre : A new segmentation method for the homogenisation of GNSS-derived IWV time-series Type de document : Article/Communication Auteurs : Annarosa Quarello , Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2020 Projets : VEGAN / Bock, Olivier Importance : 25 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse de variance
[Termes IGN] changement climatique
[Termes IGN] données GNSS
[Termes IGN] estimateur
[Termes IGN] homogénéisation
[Termes IGN] inférence
[Termes IGN] itération
[Termes IGN] positionnement ponctuel précis
[Termes IGN] programmation dynamique
[Termes IGN] série temporelle
[Termes IGN] surveillance météorologique
[Termes IGN] teneur intégrée en vapeur d'eau
[Termes IGN] variation saisonnièreRésumé : (auteur) Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data which have not yet been used in this context. This paper proposes a novel segmentation method that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm that remains the most efficient exact algorithm to estimate the change-point positions. The statistical performance of the method is assessed through numerical experiments. An application to a real data set of 120 global GNSS stations is presented. The method is implemented in the R package GNSSseg that will be available on the CRAN. Numéro de notice : P2020-005 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2005.04683 En ligne : https://doi.org/10.48550/arXiv.2005.04683 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95078
Titre : Planar maps, random walks and circle packing : École d'été de probabilités de Saint-Flour XLVIII - 2018 Type de document : Guide/Manuel Auteurs : Asaf Nachmias, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Mathematics num. 2243 Importance : 120 p. ISBN/ISSN/EAN : 978-3-030-27968-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] arbre aléatoire
[Termes IGN] fonction harmonique
[Termes IGN] graphe planaire
[Termes IGN] modèle de MarkovIndex. décimale : 23.60 Statistiques et probabilités Résumé : (Editeur) This open access book focuses on the interplay between random walks on planar maps and Koebe’s circle packing theorem. Further topics covered include electric networks, the He–Schramm theorem on infinite circle packings, uniform spanning trees of planar maps, local limits of finite planar maps and the almost sure recurrence of simple random walks on these limits. One of its main goals is to present a self-contained proof that the uniform infinite planar triangulation (UIPT) is almost surely recurrent. Full proofs of all statements are provided. A planar map is a graph that can be drawn in the plane without crossing edges, together with a specification of the cyclic ordering of the edges incident to each vertex. One widely applicable method of drawing planar graphs is given by Koebe’s circle packing theorem (1936). Various geometric properties of these drawings, such as existence of accumulation points and bounds on the radii, encode important probabilistic information, such as the recurrence/transience of simple random walks and connectivity of the uniform spanning forest. This deep connection is especially fruitful to the study of random planar maps. The book is aimed at researchers and graduate students in mathematics and is suitable for a single-semester course; only a basic knowledge of graduate level probability theory is assumed. Note de contenu : 1. Introduction
1.1 The Circle Packing Theorem
1.2 Probabilistic Applications
2. Random Walks and Electric Networks
2.1 Harmonic Functions and Voltages
2.2 Flows and Currents
2.3 The Effective Resistance of a Network
2.4 Energy
2.5 Infinite Graphs
2.6 Random Paths
2.7 Exercises
3. The CirclePacking Theorem
3.1 Planar Graphs, Maps and Embeddings
3.2 Proof of the Circle Packing Theorem
4. Parabolic and Hyperbolic Packings
4.1 Infinite Planar Maps
4.2 The Ring Lemma and Infinite Circle Packings
4.3 Statement of the He–Schramm Theorem
4.4 Proof of the He–Schramm Theorem
4.5 Exercises
5. Planar Local Graph Limits
5.1 Local Convergenceof Graphs and Maps
5.2 The Magic Lemma
5.3 Recurrence of Bounded Degree Planar Graph Limits
5.4 Exercises
6. Recurrence of Random Planar Maps
6.1 Star-Tree Transform
6.2 Stationary Random Graphs and Markings
6.3 Proof of Theorem
7. Uniform Spanning Trees of Planar Graphs
7.1 Introduction
7.2 Basic Properties of the UST
7.3 Limits over Exhaustions:The Free and Wired USF
7.4 Planar Duality
7.5 Connectivity of the Free Forest
7.6 Exercises
8. Related TopicsNuméro de notice : 26541 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-030-27968-4 En ligne : http://doi.org/10.1007/978-3-030-27968-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97764 Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)
Titre : Probabilistic pose estimation and 3D reconstruction of vehicles from stereo images Type de document : Thèse/HDR Auteurs : Maximilian Alexander Coenen, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 857 Importance : 160 p. ISBN/ISSN/EAN : 978-3-7696-5269-7 Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität HannoverLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] estimation de pose
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] reconstruction 3D
[Termes IGN] véhicule automobileRésumé : (auteur) The pose estimation and reconstruction of 3D objects from images is one of the major problems that are addressed in computer vision and photogrammetry. The understanding of a 3D scene and the 3D reconstruction of specific objects are prerequisites for many highly relevant applications of computer vision such as mobile robotics and autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable association and alignment with the 3D model. The goal of this thesis is to infer valuable observations from the images and to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of the different observation types. To achieve this goal, this thesis presents three major contributions for the particular task of 3Dvehicle reconstruction from street-level stereo images. First, a subcategory-aware deformable vehicle model is introduced that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. Second, a Convolutional Neural Network (CNN) is proposed which extracts observations from an image. In particular, the CNN is used to derive a prediction of the vehicle orientation and type, which are introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle key points and wireframes, which are well-suited for model association and model fitting. Third, the task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Suitable parametrisations and formulations of likelihood and prior terms are introduced for a joint consideration of the derived observations and prior information in the probabilistic objective function. As the objective function is non-convex and discontinuous, a proper customized strategy based on stochastic sampling is proposed for inference, yielding convincing results for the estimated poses and shapes of the vehicles. To evaluate the performance and to investigate the strengths and limitations of the proposed method, extensive experiments are conducted using two challenging real-world data sets: the publicly available KITTI benchmark and the ICSENS data set, which was created in the scope of this thesis. On both data sets, the benefit of the developed shape prior and of each of the individual components of the probabilistic model can be shown. The proposed method yields vehicle pose estimates with a median error of up to 27 cm for the position and up to 1.7◦for the orientation on the data sets. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed model and inference procedure. Numéro de notice : 17685 Affiliation des auteurs : non IGN Autre URL associée : vers ResearchGate Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geodäsie und Geoinformatik : Hanovre : 2020 DOI : 10.13140/RG.2.2.19618.86728 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-857.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98165 Réponses de la productivité des forêts aux fluctuations météorologiques : biais et surestimations des estimations de terrain / Olivier Bouriaud (2020)
Titre : Réponses de la productivité des forêts aux fluctuations météorologiques : biais et surestimations des estimations de terrain Type de document : Thèse/HDR Auteurs : Olivier Bouriaud , Auteur Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2020 Importance : 52 p. Note générale : bibliographie
Dossier présenté pour l’obtention de l’Habilitation à Diriger des Recherches, Université Paris-Sud, Ecole Doctorale Sciences du Végétal : du Gène à l'EcosystèmeLangues : Français (fre) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] allométrie
[Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] phénomène climatique extrême
[Termes IGN] productivité biologiqueRésumé : (auteur) La productivité, définie comme l’accroissement annuel en volume ou en biomasse d’un peuplement forestier, est le meilleur indicateur de sa vitalité, dont dépendent directement nombre de processus, biens et services. L’analyse de la productivité, omniprésente en sciences forestières, passe par son estimation, ce qui pose des questions méthodologiques importantes. Une question centrale de mes travaux de recherche a porté sur l’amélioration des estimations de productivité à différentes échelles spatiales et temporelles, et l’approfondissement de la compréhension des effets du climat et de la gestion sur la productivité des forêts. La croissance radiale des arbres est un des éléments les mieux étudiés et décrits dans la littérature, mais qui n’est qu’assez indirectement lié à la productivité lorsque celle-ci est exprimée en termes de biomasse ou de quantités de carbone fixés par unités de temps et de surface. Mes travaux ont montré que la raison de la perte de proportionnalité entre croissance radiale et productivité se structure en plusieurs termes : le manque de proportionnalité entre la croissance secondaire et la croissance primaire, le découplage existant entre croissance individuelle et production totale dans des communautés végétales fermées, le découplage entre la croissance radiale et la variation de la densité du bois, enfin l’échantillonnage, qui renvoie directement aux questions typiques des programmes d’inventaire forestier nationaux et qui tient donc à un axe de recherche spécifique. Tous ces mécanismes convergent vers une surestimation des fluctuations de la productivité. Sur cette base de connaissances, les travaux proposés dans mon projet s’organisent autour de deux axes : un axe portant sur l’amélioration de la quantification de la productivité, incluant une intégration des progrès dans les méthodes d’inventaire forestier national, et un axe portant sur l’analyse à très grande échelle de la productivité et de sa relation au climat, à la gestion. L’axe d’amélioration des estimations se justifie par le fait que pratiquement toutes les estimations de volume et de biomasse font appel à des modèles de biomasse ou de volume. Mais les erreurs de prédiction des modèles ont une amplitude représentant environ 10 à 40% de l’estimation elle-même. Toute amélioration des modèles offrirait donc un gain appréciable sur les prédictions. La multiplicité des sources de variation de l’allométrie impose l’utilisation de formes de modèles assez souples pour les absorber, et dont le développement est déjà en cours. L’estimation de la productivité nécessite d’utiliser en différentiel des modèles ajustés sur des données statiques. La dynamique de l’allocation aux compartiments aériens boisés n’est pas assez documentée pour être prise en compte, mais pourrait s’avérer importante quantitativement et apporter des connaissances sur le comportement et la réaction des essences aux stress. Concernant le deuxième axe, les objectifs sont de quantifier la réponse de la productivité des forêts aux évènements météorologiques à l’échelle de la ressource, en approfondissant la prise en compte de l’autocorrélation temporelle dans les estimations de productivité, et en abordant la problématique de la résistance aux évènements extrêmes. Les interactions avec la gestion seront analysées en se basant sur les progrès méthodologiques et se concentrant sur les changements de l’allométrie des couronnes et de leur intrication spatiale. De nombreuses études récentes montrent une augmentation globale de la productivité des forêts. Déterminer la part du forçage climatique et des effets de la gestion sont des objectifs déterminants des défis futurs que sont la transition climatique, et au plan de la gestion, l’antagonisme entre écologie politique, conservation de la nature et bioéconomie. Numéro de notice : 17534 Affiliation des auteurs : LIF (2020- ) Thématique : FORET/MATHEMATIQUE Nature : HDR Note de thèse : HDR : Sciences du végétal : Paris-Sud : 2020 nature-HAL : HDR DOI : sans Date de publication en ligne : 27/01/2021 En ligne : https://hal.archives-ouvertes.fr/tel-03123055/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98238 Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
[article]
Titre : Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Li Mi, Auteur ; Zhenzhong Chen, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image à très haute résolution
[Termes IGN] processus stochastique
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (Auteur) Semantic segmentation plays an important role in remote sensing image understanding. Great progress has been made in this area with the development of Deep Convolutional Neural Networks (DCNNs). However, due to the complexity of ground objects’ spectrum, DCNNs with simple classifier have difficulties in distinguishing ground object categories even though they can represent image features effectively. Additionally, DCNN-based semantic segmentation methods learn to accumulate contextual information over large receptive fields that causes blur on object boundaries. In this work, a novel approach named Superpixel-enhanced Deep Neural Forest (SDNF) is proposed to target the aforementioned problems. To improve the classification ability, we introduce Deep Neural Forest (DNF), where the representation learning of deep neural network is conducted by a completely differentiable decision forest. Therefore, better classification accuracy is achieved by combining DCNNs with decision forests in an end-to-end manner. In addition, considering the homogeneity within superpixels and heterogeneity between superpixels, a Superpixel-enhanced Region Module (SRM) is proposed to further alleviate the noises and strengthen edges of ground objects. Experimental results on the ISPRS 2D semantic labeling benchmark demonstrate that our model significantly outperforms state-of-the-art methods thus validate the efficiency of our proposed SDNF. Numéro de notice : A2020-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.006 Date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94403
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Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkPermalinkEvaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)PermalinkPermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkFostering the use of methods for geosimulation models sensitivity analysis and validation / Romain Reuillon (2019)PermalinkImproving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])PermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkPermalinkProjection sur l’évolution de la distribution future de la population en utilisant du Machine Learning et de la géosimulation / Julie Grosmaire (2019)PermalinkSignaux et systèmes / André Quinquis (2019)PermalinkSimultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)PermalinkPermalinkThe French NFI : flexibility at the heart of the design / François Morneau (2019)PermalinkPermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkDetection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkGénération d'observations pour la validation ou la comparaison de logiciels d'ajustement de mesures par moindres carrés / Stéphane Durand in XYZ, n° 157 (décembre 2018 - février 2019)PermalinkOn the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)PermalinkAutomated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)PermalinkDeep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkDeveloping allometric equations for estimating shrub biomass in a Boreal Fen / Annie He in Forests, vol 9 n° 9 (September 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkA two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkUncertainty 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)PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)PermalinkEstimating storm damage with the help of low-altitude photographs and different sampling designs and estimators / Pekka Hyvönen in Silva fennica, vol 52 n° 3 ([01/08/2018])PermalinkSpectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)PermalinkParametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)PermalinkStochastic models in the DORIS position time series : estimates for IDS contribution to ITRF2014 / Anna Klos in Journal of geodesy, vol 92 n° 7 (July 2018)PermalinkGeometric reasoning with uncertain polygonal faces / Jochen Meidow in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkExploring the sensitivity of coastal inundation modelling to DEM vertical error / Harry West in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkGeodetic VLBI with an artificial radio source on the Moon : a simulation study / Grzegorz Klopotek in Journal of geodesy, vol 92 n° 5 (May 2018)PermalinkSeed dispersal, microsites or competition : what drives gap regeneration in an old-growth forest? An application of spatial point process modelling / Georg Gratzer in Forests, vol 9 n° 5 (May 2018)PermalinkContextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)PermalinkEstimated location of the seafloor sources of marine natural oil seeps from sea surface outbreaks : A new "source path procedure" applied to the northern Gulf of Mexico / Zhour Najoui in Marine and Petroleum Geology, Vol 91 (March 2018)PermalinkEvaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA / Christian Kuehne in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkEPLA : efficient personal location anonymity / Dapeng Zhao in Geoinformatica, vol 22 n° 1 (January 2018)PermalinkNouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkBayesian statistics and Monte Carlo methods / Karl Rudolf Koch in Journal of geodetic science, vol 8 n° 1 (January 2018)PermalinkCrop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)PermalinkDeep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)PermalinkDesign and implementation of a model predictive observer for AHRS / Jafar Keighobadi in GPS solutions, vol 22 n° 1 (January 2018)PermalinkPermalinkPermalinkUn inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)PermalinkMarkov random field for combined defogging and stereo reconstruction / Laurent Caraffa (2018)PermalinkPermalinkPermalinkPermalinkPermalinkA wavelet decomposition and polynomial fitting-based method for the estimation of time-varying residual motion error in airborne interferometric SAR / Hai Qiang Fu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)PermalinkAlgebraic method to speed up robust algorithms: example of laser-scanned point clouds / B. Palancz in Survey review, vol 49 n° 357 (December 2017)PermalinkEstimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkLearning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkSystematic error mitigation in multi-GNSS positioning based on semiparametric estimation / Wenkun Yu in Journal of geodesy, vol 91 n° 12 (December 2017)PermalinkTotal evaporation estimation for accurate water accounting purposes: an appraisal of various available estimation methods / Cletah Shoko in Geocarto international, vol 32 n° 12 (December 2017)PermalinkMapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar / Matthew Sumnall in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkCharacterizing noise in daily GPS position time series with overlapping Hadamard variance and maximum likelihood estimation / Chang Xu in Survey review, vol 49 n° 355 (October 2017)PermalinkEfficient structure from motion for oblique UAV images based on maximal spanning tree expansion / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)PermalinkLocalisation des caméras ANPR sur le réseau routier pour le profilage géographique / Marie Trotta in Revue internationale de géomatique, vol 27 n° 4 (octobre - décembre 2017)PermalinkMulti-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data / Hooman Latifi in Forestry, an international journal of forest research, vol 90 n° 4 (October 2017)PermalinkRegistration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)PermalinkTree size thresholds produce biased estimates of forest biomass dynamics / Eric B. Searle in Forest ecology and management, vol 400 (15 September 2017)PermalinkAssessing the performance of multi-GNSS precise point positioning in Asia-Pacific region / X. 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Rey in Transactions in GIS, vol 21 n° 4 (August 2017)PermalinkA higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkHybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar / Sören Holm in Remote sensing of environment, vol 197 (August 2017)PermalinkModeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkForest modelling: the gamma shape mixture model and simulation of tree diameter distributions / Rafał Podlaski in Annals of Forest Science, vol 74 n° 2 (June 2017)PermalinkGPS coordinate time series measurements in Ontario and Quebec, Canada / Hadis Samadi Alinia in Journal of geodesy, vol 91 n° 6 (June 2017)PermalinkPerformance evaluation of land change simulation models using landscape metrics / Sadeq Dezhkam in Geocarto international, vol 32 n° 6 (June 2017)PermalinkUncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations / Wolfgang Niemeier in Journal of applied geodesy, vol 11 n° 2 (June 2017)Permalink3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkExploring spatiotemporal clusters based on extended kernel estimation methods / Jay Lee in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkInverting Glacial Isostatic Adjustment signal using Bayesian framework and two linearly relaxing rheologies / Lambert Caron in Geophysical journal international, vol 209 n° 2 (May 2017)PermalinkForest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)PermalinkHyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkIntegrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)PermalinkIntegrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry-air atmospheric profiles / Jakob Schwarz in Earth and space science, vol 4 n° 4 (April 2017)PermalinkMinimizing construction emissions using Building Information Modeling and Decision-Making techniques / Mohamed Marzouk in International journal of 3-D information modeling, vol 6 n° 2 (April-June 2017)PermalinkPerformance evaluation of GNSS-TEC estimation techniques at the grid point in middle and low latitudes during different geomagnetic conditions / O. E. Abe in Journal of geodesy, vol 91 n° 4 (April 2017)PermalinkDetermining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality / Kyle J. Eyvindson in Annals of Forest Science, vol 74 n° 1 (March 2017)PermalinkEstimation and analysis of Galileo differential code biases / Min Li in Journal of geodesy, vol 91 n° 3 (March 2017)PermalinkImage-based target detection and radial velocity estimation methods for multichannel SAR-GMTI / Kei Suwa in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkMapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory / Jonas Bohlin in Silva fennica, vol 51 n° 2 (2017)PermalinkSemi-parametric segmentation of multiple series using a DP-Lasso strategy / Karine Bertin in Journal of Statistical Computation and Simulation, vol 87 n° 6 (2017)PermalinkInconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions / Yan Li in Scientific reports, vol 7 (2017)PermalinkAmélioration de la vitesse et de la qualité d'image du rendu basé image / Rodrigo Ortiz Cayón (2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkPermalinkComparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)PermalinkComputationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process / Xing Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkHyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkPermalinkModèles géographiques avec le langage Mathematica / André Dauphiné (2017)PermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkOndelettes et processus stochastiques / Abdourrahmane M. 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Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkUtilisation d’un modèle numérique de hauteur en stratification des données de l’Inventaire Forestier National / Sophie Georges (2017)PermalinkVision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)PermalinkComparison of methods used in European National Forest Inventories for the estimation of volume increment: towards harmonisation / Thomas Gschwantner in Annals of Forest Science, vol 73 n° 4 (December 2016)PermalinkDetermination of a terrestrial reference frame via Kalman filtering of very long baseline interferometry data / Benedikt Soja in Journal of geodesy, vol 90 n° 12 (December 2016)PermalinkA drift line bias estimator: ARMA-based filter or calibration method, and its application in BDS/GPS-based attitude determination / Zhang Liang in Journal of geodesy, vol 90 n° 12 (December 2016)PermalinkSystematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)PermalinkThe effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass / Ronald E. McRoberts in Annals of Forest Science, vol 73 n° 4 (December 2016)PermalinkAn advanced GNSS code multipath detection and estimation algorithm / Negin Sokhandan in GPS solutions, vol 20 n° 4 (October 2016)PermalinkDisaster debris estimation using high-resolution polarimetric stereo-SAR / Christian N. Koyama in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkModeling the effects of horizontal positional error on classification accuracy statistics / Henry B. Glick in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 10 (October 2016)PermalinkOutlier detection by using fault detection and isolation techniques in geodetic networks / U.M. Durdag in Survey review, vol 48 n° 351 (October 2016)PermalinkA probabilistic approach to detect mixed periodic patterns from moving object data / Jun Li in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkRelative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation / Alyssa Endres in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkTaking correlations in GPS least squares adjustments into account with a diagonal covariance matrix / Gaël Kermarrec in Journal of geodesy, vol 90 n° 9 (September 2016)PermalinkAn adaptive stochastic model for GPS observations and its performance in precise point positioning / J. Z. Zheng in Survey review, vol 48 n° 349 (July 2016)PermalinkSpace-time multiple regression model for grid-based population estimation in urban areas / Ko Ko Lwin in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkStochastic modeling of triple-frequency BeiDou signals: estimation, assessment and impact analysis / Bofeng Li in Journal of geodesy, vol 90 n° 7 (July 2016)PermalinkComparison of robust estimators for leveling networks in Monte Carlo simulations / Maria Pokarowska in Reports on geodesy and geoinformatics, vol 101 (June 2016)PermalinkInventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods / X. Tang in Annals of forest research, vol 59 n° 2 (July - December 2016)PermalinkThe variants of an LOD of a 3D building model and their influence on spatial analyses / Filip Biljecki in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkGenerative models for road network reconstruction / Colin Kuntzsch in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkReconstruction of itineraries from annotated text with an informed spanning tree algorithm / Ludovic Moncla in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkA correctly weighted least squares adjustment - Part 3 Estimating standard errors in angular observations / Charles D. Ghilani in xyHt, vol 2016 n° 4 (April 2016)PermalinkJoint analysis of GOCE gravity gradients data of gravitational potential and of gravity with seismological and geodynamic observations to infer mantle properties / Marianne Greff-Lefftz in Geophysical journal international, vol 205 n° 1 (April 2016)PermalinkAn average error-ellipsoid model for evaluating TLS point-cloud accuracy / Xijiang Chen in Photogrammetric record, vol 31 n° 153 (March - May 2016)PermalinkApproximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkMarkov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image / L. K. Tiwari in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkMIDAS robust trend estimator for accurate GPS station velocities without step detection / Geoffrey Blewitt in Journal of geophysical research : Solid Earth, vol 121 n° 3 (March 2016)PermalinkRobust spatial approximation of laser scanner point clouds by means of Free-form Curve approaches in deformation analysis / Johannes Bureick in Journal of applied geodesy, vol 10 n° 1 (March 2016)PermalinkA correctly weighted least squares adjustment - Part 2 Estimating uncertainties / Charles D. Ghilani in xyHt, vol 2016 n° 2 (February 2016)PermalinkLa géostatistique : une vision novatrice au service des géosciences / Bernard Bourgine in Géosciences, n°20 (février 2016)PermalinkSpace–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkUse of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation / Göran Stahl in Forest ecosystems, vol 3 (2016)PermalinkApplication of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania / Mercy Ojoyi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkPermalinkCaractérisation des signaux et des bruits des séries temporelles du géocentre et des paramètres de rotation de la Terre (EOP) / Bachir Gourine in Bulletin des sciences géographiques, n° 30 (2015 - 2016)PermalinkConvex programming approach to robust estimation of a multivariate Gaussian model / Samuel Balmand (2016)PermalinkPermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkPermalinkPermalinkPermalinkInvestigating efficacy of robust M-estimation of deformation from observation differences / Krzysztof Nowel in Survey review, vol 48 n° 346 (January 2016)PermalinkLandmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks / Bahman Soheilian (2016)PermalinkLocalisation à base d’amers visuels : Cartographie et mise en correspondance de marquages au sol et intégration dans LBA / Bahman Soheilian (2016)PermalinkModelling forest canopy trends with on-demand spatial simulation / Gordon M. Green in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkMultifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)PermalinkOn estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)PermalinkPassive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E / Jinyang Du in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPhotogrammetric computer vision / Wolfgang Förstner (2016)PermalinkA probabilistic approach for InSAR time-series postprocessing / Ling Chang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPermalinkQualité des données géographiques : à propos de la propagation des incertitudes / Gilles Troispoux in Signature, n° 59 (janvier 2016)PermalinkPermalinkPermalink