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A method for discovery and analysis of temporal patterns in complex event data / Donna J. Peuquet in International journal of geographical information science IJGIS, vol 29 n° 9 (September 2015)
[article]
Titre : A method for discovery and analysis of temporal patterns in complex event data Type de document : Article/Communication Auteurs : Donna J. Peuquet, Auteur ; Anthony C. Robinson, Auteur ; Samuel Stehle, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1588 - 1611 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] découverte de connaissances
[Termes IGN] données spatiotemporelles
[Termes IGN] événement
[Termes IGN] extension temporelleMots-clés libres : T-pattern analysis STempo Résumé : (Auteur) Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis. Numéro de notice : A2015-608 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1042380 En ligne : https://doi.org/10.1080/13658816.2015.1042380 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78037
in International journal of geographical information science IJGIS > vol 29 n° 9 (September 2015) . - pp 1588 - 1611[article]Estimation of geographical databases capture scale based on inter-vertices distances exploration / Jean-François Girres in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)
[article]
Titre : Estimation of geographical databases capture scale based on inter-vertices distances exploration Type de document : Article/Communication Auteurs : Jean-François Girres , Auteur Année de publication : 2015 Conférence : ISPRS 2015, Geospatial Week : Laserscanning, ISSDQ, CMRT, ISA, GeoVIS, GeoBigData 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Annals, GeoVIS 2015 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Annals Article en page(s) : pp 305 - 310 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données localisées
[Termes IGN] caractérisation
[Termes IGN] découverte de connaissances
[Termes IGN] niveau de détailRésumé : (auteur) This article presents a method to estimate the capture scale of a geographical database based on the characterization of its level of detail. This contribution fits in a larger research, dealing with the development of a general model to estimate the imprecision of length and area measurements computed from the geometry of objects of weakly informed geographical databases. In order to parameterize automatically a digitizing error simulation model, the characteristic capture scale is required. Thus, after a definition of the different notions of scales in geographical databases, the proposed method is presented. The goal of the method is to model the relation between the level of detail of a geographical database, by exploring inter-vertices distances, and its characteristic capture scale. To calibrate the model, a digitizing test experiment is provided, showing a clear relation between median intervertices distance and characteristic capture scale. The proposed knowledge extraction method proves to be the usefull in order to parameterize the measurement imprecision estimation model, and more generally to inform the database user when the capture scale is unknown. Nevertheless, further experiments need to be provided to improve the method, and model the relation between level of detail and capture scale with more efficiency. Numéro de notice : A2015--115 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W5-305-2015 Date de publication en ligne : 20/08/2015 En ligne : https://doi.org/10.5194/isprsannals-II-3-W5-305-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91808
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W5 (October 2015) . - pp 305 - 310[article]A knowledge base to classify and mix 3D rendering styles / Mickaël Brasebin in Revista Brasileira de Cartografia, Geodésia, Fotogrametria e Sensoriamento Remoto, vol 5 n° 67 (August 2015)
[article]
Titre : A knowledge base to classify and mix 3D rendering styles Titre original : Base de Conhecimento para Classificação e Fusão de Estilos em Renderizações 3D Type de document : Article/Communication Auteurs : Mickaël Brasebin , Auteur ; Sidonie Christophe , Auteur ; Elodie Buard , Auteur ; Florian Pelloie, Auteur Année de publication : 2015 Conférence : ICC 2015, 27th International Cartographic Conference, 16th General Assembly 23/08/2015 28/08/2015 Rio de Janeiro Brésil open access proceedings Article en page(s) : pp 1067 - 1077 Langues : Anglais (eng) Descripteur : [Termes IGN] base de connaissances
[Termes IGN] données localisées 3D
[Termes IGN] rendu (géovisualisation)
[Termes IGN] sémiologie graphique
[Termes IGN] style cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) In cartography, good practices are clearly established whereas they are not clearly defined for 3D (geographical) renderings. This article details some very first researches and an agenda that aims to provide a style knowledge database that offers possibilities to classify renderings according to graphical patterns. One application is to provide a method to generate relevant transition between two different styles to ease navigation in 3D geographical environment. Note de contenu : Special Issue 27th ICC. Numéro de notice : A2015--054 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans En ligne : http://www.lsie.unb.br/rbc/index.php?journal=rbc&page=article&op=view&path%5B%5D [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83162
in Revista Brasileira de Cartografia, Geodésia, Fotogrametria e Sensoriamento Remoto > vol 5 n° 67 (August 2015) . - pp 1067 - 1077[article]Documents numériques
en open access
A knowledge base to classify and mix 3D rendering stylesAdobe Acrobat PDF Semisupervised transfer component analysis for domain adaptation in remote sensing image classification / Giona Matasci in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
[article]
Titre : Semisupervised transfer component analysis for domain adaptation in remote sensing image classification Type de document : Article/Communication Auteurs : Giona Matasci, Auteur ; Michele Volpi, Auteur ; Mikhail Kanevski, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3550 - 3564 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] classification à base de connaissances
[Termes IGN] classification automatique
[Termes IGN] découverte de connaissances
[Termes IGN] extraction automatique
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] occupation du solRésumé : (Auteur) In this paper, we study the problem of feature extraction for knowledge transfer between multiple remotely sensed images in the context of land-cover classification. Several factors such as illumination, atmospheric, and ground conditions cause radiometric differences between images of similar scenes acquired on different geographical areas or over the same scene but at different time instants. Accordingly, a change in the probability distributions of the classes is observed. The purpose of this work is to statistically align in the feature space an image of interest that still has to be classified (the target image) to another image whose ground truth is already available (the source image). Following a specifically designed feature extraction step applied to both images, we show that classifiers trained on the source image can successfully predict the classes of the target image despite the shift that has occurred. In this context, we analyze a recently proposed domain adaptation method aiming at reducing the distance between domains, Transfer Component Analysis, and assess the potential of its unsupervised and semisupervised implementations. In particular, with a dedicated study of its key additional objectives, namely the alignment of the projection with the labels and the preservation of the local data structures, we demonstrate the advantages of Semisupervised Transfer Component Analysis. We compare this approach with other both linear and kernel-based feature extraction techniques. Experiments on multi- and hyperspectral acquisitions show remarkable cross- image classification performances for the considered strategy, thus confirming its suitability when applied to remotely sensed images. Numéro de notice : A2015-319 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2377785 En ligne : https://doi.org/10.1109/TGRS.2014.2377785 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76570
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3550 - 3564[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning / Junwei Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
[article]
Titre : Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning Type de document : Article/Communication Auteurs : Junwei Han, Auteur ; Dingwen Zhang, Auteur ; Gong Cheng, Auteur Année de publication : 2015 Article en page(s) : pp 3325 - 3337 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] détection d'objet
[Termes IGN] estimation bayesienne
[Termes IGN] état de l'art
[Termes IGN] moteur d'inférenceRésumé : (Auteur) The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches. Numéro de notice : A2015 - 283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2374218 Date de publication en ligne : 18/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2374218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76400
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3325 - 3337[article]Réservation
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