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Enabling geovisual analytics of health data using a server-side approach / Ulanbek Turdukulov in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)
[article]
Titre : Enabling geovisual analytics of health data using a server-side approach Type de document : Article/Communication Auteurs : Ulanbek Turdukulov, Auteur ; Simon Moncrieff, Auteur Année de publication : 2016 Article en page(s) : pp 16 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse de données
[Termes IGN] analyse multivariée
[Termes IGN] géomatique web
[Termes IGN] géovisualisation
[Termes IGN] interface utilisateur
[Termes IGN] santé
[Termes IGN] serveur web
[Termes IGN] style graphiqueRésumé : (auteur) Geovisual analytics can enable a user to explore multivariate spatio-temporal health datasets and to understand spatial distribution of diseases especially in relation to external factors that may influence outbreaks. External data are presently distributed using geo web services. Web services are used in health mainly to present results leading to a supplier-driven service model limiting the exploration of health data. In this paper, we illustrate server-side approach of designing a geovisual analytics environment that allows user-driven geovisual analytics. The server-side combines a data query, processing technique, and styling methodology to rapidly visually summarize properties of a dataset. We illustrate this functionality on a typical workflow used by a health researcher and demonstrate analytical functionality in cases where a consistent classification and styling scheme is needed across dynamically aggregated multivariate spatio-temporal datasets. Since the framework builds on the existing Open Geospatial Consortium web mapping standards, it integrates the existing geo web services as well as stand-alone health data repositories into an infrastructure that allows combination and interactive exploration of these heterogeneous datasets in a visual environment. Numéro de notice : A2016-140 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1065762 En ligne : https://doi.org/10.1080/15230406.2015.1065762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80322
in Cartography and Geographic Information Science > Vol 43 n° 1 (January 2016) . - pp 16 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2016011 RAB Revue Centre de documentation En réserve L003 Disponible European handbook of crowdsourced geographic information, ch. 12. Gaining knowledge from georeferenced social media data with visual analytics / Gennady Andrienko (2016)
Titre de série : European handbook of crowdsourced geographic information, ch. 12 Titre : Gaining knowledge from georeferenced social media data with visual analytics Type de document : Chapitre/Contribution Auteurs : Gennady Andrienko, Auteur ; Natalia Andrienko, Auteur Editeur : Londres : Ubiquity press Année de publication : 2016 Importance : pp 157 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] analyse géovisuelle
[Termes IGN] analyse spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] mobilité urbaine
[Termes IGN] réseau social
[Termes IGN] trajet (mobilité)
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Analysis of the collections of geographically referenced posts published in social media, such as Twitter, Flickr, and YouTube, can bring new knowledge about places, geographical objects, and events interesting to people, and about people’s mobility behaviours. Gaining knowledge from large data collections requires combining computational analysis with human interpretation, judgement, and reasoning, which, in turn, require appropriate visual representations of the data and analysis results. Visual analytics integrates computational analysis techniques with interactive visual interfaces to support collaborative human−computer analytical activities. We give a brief overview of visual analytics approaches to extracting various kinds of information and knowledge from georeferenced social media data. Numéro de notice : H2016-003 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.5334/bax En ligne : https://doi.org/10.5334/bax Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83785 Documents numériques
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Gaining knowledge from georeferenced social media dataAdobe Acrobat PDF Multifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)
Titre : Multifractal analysis for multivariate data with application to remote sensing Type de document : Thèse/HDR Auteurs : Sébastien Combrexelle, Auteur ; Jean-Yves Tourneret, Directeur de thèse ; Steve Mclaughlin, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2016 Importance : 211 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse fractale
[Termes IGN] analyse multivariée
[Termes IGN] approche hiérarchique
[Termes IGN] estimation bayesienne
[Termes IGN] image hyperspectrale
[Termes IGN] modèle statistique
[Termes IGN] télédétection
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Texture characterization is a central element in many image processing applications. Texture analysis
can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the wavelet coefficients or wavelet leaders. Although successfully applied in various contexts, multifractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small image sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to
embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefits of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging.Note de contenu : Introduction
1- Multifractal analysis
2- Statistical model and univariate Bayesian estimation
3- Bayesian multifractal analysis of
multivariate imagesNuméro de notice : 25811 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2016 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) En ligne : http://www.theses.fr/2016INPT0078 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95074 Remote Sensing Observations of Continental Surfaces, ch. 6. Airborne lidar data processing / Clément Mallet (2016)
contenu dans Remote Sensing Observations of Continental Surfaces, vol 1. Optical Remote Sensing of Land Surface / Nicolas Baghdadi (2016)
Titre de série : Remote Sensing Observations of Continental Surfaces, ch. 6 Titre : Airborne lidar data processing Type de document : Chapitre/Contribution Auteurs : Clément Mallet , Auteur ; Nesrine Chehata , Auteur ; Jean-Stéphane Bailly, Auteur Editeur : Londres : ISTE Press Année de publication : 2016 Importance : pp 249 - 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse en composantes principales
[Termes IGN] attribut
[Termes IGN] classification
[Termes IGN] déconvolution
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde
[Termes IGN] ondelette
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] signal laserRésumé : (auteur) This chapter introduces the main data analysis methods associated with topographic and bathymetric airborne LiDAR systems. Data delivered by these sensors can be of two types: the majority of commercial systems deliver three-dimensional (3D) point clouds (systems referred to as multiecho), whereas a limited number directly provides the whole laser signal backscattered by the Earth surface (systems referred to as full-waveform (FW)). Numéro de notice : H2016-009 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1016/B978-1-78548-102-4.50006-5 Date de publication en ligne : 07/10/2016 En ligne : https://doi.org/10.1016/B978-1-78548-102-4.50006-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91789
Titre : Spatial machine learning applied to multivariate and multimodal images Type de document : Thèse/HDR Auteurs : Gianni Franchi, Auteur ; Jesus Angulo lopez, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2016 Importance : 197 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Recherche Paris Sciences et Lettres, préparée à MINES ParisTech, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] krigeage
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] microscope électronique
[Termes IGN] morphologie mathématique
[Termes IGN] régression linéaireIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis focuses on multivariate spatial statistics and machine learning applied to hyperspectral and multimodal and images in remote sensing and scanning electron
microscopy (SEM). In this thesis the following topics are considered:
Fusion of images: SEM allows us to acquire images from a given sample using different modalities. The purpose of these studies is to analyze the interest of fusion of information to improve the multimodal SEM images acquisition. We have modeled
and implemented various techniques of image fusion of information, based in
particular on spatial regression theory. They have been assessed on various
datasets.
Spatial classification of multivariate image pixels: We have proposed a novel approach for pixel classification in multi/hyperspectral images. The aim of this technique is to represent and efficiently describe the spatial/spectral features of multivariate images. These multi-scale deep descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations.
Spatial dimensionality reduction: We have developed a technique to extract a feature space using morphological principal component analysis. Indeed, in order to take into account the spatial and structural information we used mathematical morphology operatorsNote de contenu : I- Introduction
II- Feature representation and classification for hyperspectral images
III- Fusion of information for multimodal SEM images
IV ConclusionNuméro de notice : 25828 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Morphologie Mathématique : Paris, 2016 nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01483980v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95124 Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin / M.D. Adams in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkThe iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds / Jan Böhm (2016)PermalinkVegetation classification and biogeography of European floodplain forests and alder carrs / Jan Douda in Applied Vegetation Science, vol 19 n° 1 (January 2016)PermalinkDiscrimination of deciduous tree species from time series of unmanned aerial system imagery / Jonathan Lisein in Plos one, vol 10 n° 11 (November 2015)PermalinkAPFiLoc: An Infrastructure-Free Indoor Localization method fusing smartphone inertial sensors, landmarks and map information / Jianga Shang in Sensors, vol 15 n° 10 (October 2015)PermalinkLeveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkPolygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkImpact of the North Atlantic oscillation on Southern Europe water distribution: insights from geodetic data / Pierre Valty in Earth Interactions, vol 19 n° 10 (September 2015)PermalinkRegional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine / Dmytro Movchan in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)Permalink