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Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction / Torstein Fjeldstad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
[article]
Titre : Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction Type de document : Article/Communication Auteurs : Torstein Fjeldstad, Auteur ; Henning Omre, Auteur Année de publication : 2020 Article en page(s) : pp 1957 - 1968 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] amplitude
[Termes IGN] analyse mathématique
[Termes IGN] approximation
[Termes IGN] chaîne de Markov
[Termes IGN] filtrage numérique d'image
[Termes IGN] lithologie
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle d'inversion
[Termes IGN] modèle mathématique
[Termes IGN] processus gaussien
[Termes IGN] sismicitéRésumé : (Auteur) The efficient assessment of convolved hidden Markov models is discussed. The bottom layer is defined as an unobservable categorical first-order Markov chain, whereas the middle layer is assumed to be a Gaussian spatial variable conditional on the bottom layer. Hence, this layer appears marginally as a Gaussian mixture spatial variable. We observe the top layer as a convolution of the middle layer with Gaussian errors. The focus is on assessing the categorical and Gaussian mixture variables given the observations, and we operate in a Bayesian inversion framework. The model is defined to perform the inversion of subsurface seismic amplitude-versus-offset data into lithology/fluid classes and to assess the associated seismic material properties. Due to the spatial coupling in the likelihood functions, evaluation of the posterior normalizing constant is computationally demanding, and brute-force, single-site updating Markov chain Monte Carlo (MCMC) algorithms converge far too slowly to be useful. We construct two classes of approximate posterior models, which we assess analytically and efficiently using the recursive forward–backward algorithm. These approximate posterior densities are used as proposal densities in an independent proposal MCMC algorithm to determine the correct posterior model. A set of synthetic realistic examples is presented. The proposed approximations provide efficient proposal densities, which results in acceptance probabilities in the range 0.10–0.50 in the MCMC algorithm. A case study of lithology/fluid seismic inversion is presented. The lithology/fluid classes and the seismic material properties can be reliably predicted. Numéro de notice : A2020-093 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2951205 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2951205 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94667
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1957 - 1968[article]Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)
[article]
Titre : Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Di Liu, Auteur ; Jonathan Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 147 - 163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] balayage laser
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau routier
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobileRésumé : (Auteur) The representation of 3D data is the key issue for shape analysis. However, most of the existing representations suffer from high computational cost and structure information loss. This paper presents a novel sequential slice representation with an attention-embedding network, named RSSNet, for 3D point cloud recognition and retrieval in road environments. RSSNet has two main branches. Firstly, a sequential slice module is designed to map disordered 3D point clouds to ordered sequence of shallow feature vectors. A gated recurrent unit (GRU) module is applied to encode the spatial and content information of these sequential vectors. The second branch consists of a key-point based graph convolution network (GCN) with an embedding attention strategy to fuse the sequential and global features to refine the structure discriminability. Three datasets were used to evaluate the proposed method, one acquired by our mobile laser scanning (MLS) system and two public datasets (KITTI and Sydney Urban Objects). Experimental results indicated that the proposed method achieved better performance than recognition and retrieval state-of-the-art methods. RSSNet provided recognition rates of 98.08%, 95.77% and 70.83% for the above three datasets, respectively. For the retrieval task, RSSNet obtained excellent mAP values of 95.56%, 87.16% and 69.99% on three datasets, respectively. Numéro de notice : A2020-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.003 Date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94582
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 147 - 163[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Real-time mapping of natural disasters using citizen update streams / Iranga Subasinghe in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)
[article]
Titre : Real-time mapping of natural disasters using citizen update streams Type de document : Article/Communication Auteurs : Iranga Subasinghe, Auteur ; Silvia Nittel, Auteur ; Michael Cressey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 393 - 421 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] cartographie collaborative
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal
[Termes IGN] diagramme de Voronoï
[Termes IGN] données localisées des bénévoles
[Termes IGN] effondrement de terrain
[Termes IGN] incendie
[Termes IGN] inondation
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] système multi-agents
[Termes IGN] tempête
[Termes IGN] temps réel
[Termes IGN] ville intelligenteRésumé : (auteur) Natural disasters such as flooding, wildfires, and mudslides are rare events, but they affect citizens at unpredictable times and the impact on human life can be significant. Citizens located close to events can provide detailed, real-time data streams capturing their event response. Instead of visualizing individual updates, an integrated spatiotemporal map yields ‘big picture’ event information. We investigate the question of whether information from affected citizens is sufficient to generate a map of an unfolding natural disaster. We built the Citizen Disaster Reaction Multi-Agent Simulation (CDR-MAS), a multi-agent system that simulates the reaction of citizens to a natural disaster in an urban region. We proposed an rkNN classification algorithm to aggregate the update streams into a series of colored Voronoi event maps. We simulated the 2018 Montecito Creek mudslide and customized the CDR-MAS with the local environment to systematically generate stream data sets. Our experimental evaluation showed that event mapping based on citizen update streams is significantly influenced by the amount of citizen participation and movement. Compared with a baseline of 100% participation, with 40% citizen participation, the event region was predicted with 40% accuracy, showing that citizen update streams can provide timely information in a smart city. Numéro de notice : A2020-031 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1639185 Date de publication en ligne : 15/07/2019 En ligne : https://doi.org/10.1080/13658816.2019.1639185 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94486
in International journal of geographical information science IJGIS > vol 34 n° 2 (February 2020) . - pp 393 - 421[article]
[article]
Titre : A survey on graph kernels Type de document : Article/Communication Auteurs : Nils M. Kriege, Auteur ; Fredrik D. Johansson, Auteur ; Christopher Morris, Auteur Année de publication : 2020 Article en page(s) : n° 5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] graphe
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] réseau neuronal de graphesRésumé : (auteur) Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification. Numéro de notice : A2020-858 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s41109-019-0195-3 Date de publication en ligne : 14/01/2020 En ligne : https://doi.org/10.1007/s41109-019-0195-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98905
in Applied network science > vol 5 (2020) . - n° 5[article]
Titre : Fast computation of distances in a tree Titre original : Calcul rapide de distances dans un arbre Type de document : Article/Communication Auteurs : Marc Pierrot-Deseilligny , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2020 Importance : 8 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Algorithmique
[Termes IGN] arbre (mathématique)
[Termes IGN] distance (mathématique)Résumé : (Auteur) Computation of distances between two submits of a tree is an operation that occurs in some pattern recognition problem. When this operation has to be done thousands of times on millions of trees, the linear standard algorithms in OpN q for each pair may be a bottleneck to the global computation. This note present recursive spliting method with a complexity of OplogpN qq on each pair in worst case, and Op1q in average on all pair, once a pre-computation OpN logpN qq has been done on the whole tree. A commented C++ implementation is published as a companion to this note. Numéro de notice : P2020-004 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans Date de publication en ligne : 05/05/2020 En ligne : https://hal.science/hal-02563859 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95036 Documents numériques
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