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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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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)Permalink