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Towards qualitative geovisual analytics: A case study involving places, people, and mediated experience / Ryan Burns in Cartographica, vol 48 n° 3 (October 2013)
[article]
Titre : Towards qualitative geovisual analytics: A case study involving places, people, and mediated experience Type de document : Article/Communication Auteurs : Ryan Burns, Auteur ; André Skupin, Auteur Année de publication : 2013 Article en page(s) : pp 157 - 176 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] analyse visuelle
[Termes IGN] carte de Kohonen
[Termes IGN] données qualitatives
[Termes IGN] retour d'expérience
[Termes IGN] système d'information géographique
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] GéovisualisationRésumé : (Auteur) This article seeks to address the gap between the quantitative, summative data that are typically engaged in geovisual analytics and the more personal, experiential ways of knowledge construction accentuated by qualitative GIS. By incorporating diverse forms of data within a high-dimensional conceptual framework, we set out a type of qualitative geovisual analytics. This approach is attentive to the epistemological limitations of singular data sources and highlights the multiple ways of exploring neighbourhoods. The article reports on a project that used an online survey, including collection of personal impressions of San Diego neighbourhoods based on street-level video. Three attribute spaces are conceptualized: survey respondents' characteristics, attributes of San Diego neighbourhoods, and characteristics of the words used to describe these neighbourhoods. The self-organizing map (SOM) technique was used to reduce the dimensionality of these attribute spaces for visual exploration. This approach makes several important contributions, including a demonstration of “scaling up” the work that has been done in qualitative GIS. It productively integrates experiential data with a geovisual analytics approach and reaffirms the multiple meanings of visualization. Numéro de notice : A2013-570 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.3138/carto.48.3.1691 En ligne : https://doi.org/10.3138/carto.48.3.1691 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32706
in Cartographica > vol 48 n° 3 (October 2013) . - pp 157 - 176[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2013031 RAB Revue Centre de documentation En réserve L003 Disponible Visual discovery of synchronisation in weather data at multiple temporal resolutions / Xiaojing Wu in Cartographic journal (the), vol 50 n° 3 (August 2013)
[article]
Titre : Visual discovery of synchronisation in weather data at multiple temporal resolutions Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Raul Zurita-Milla, Auteur ; Menno-Jan Kraak, Auteur Année de publication : 2013 Article en page(s) : pp 247 - 256 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] calcul matriciel
[Termes IGN] carte de Kohonen
[Termes IGN] distribution spatiale
[Termes IGN] données hétérogènes
[Termes IGN] données météorologiques
[Termes IGN] données multitemporelles
[Termes IGN] série temporelle
[Termes IGN] synchronisationRésumé : (Auteur) Analysing spatio-temporal weather patterns is fundamental to better understand the system Earth. Such patterns depend on the spatial and temporal resolution of the available data. Here, we study a particular spatio-temporal pattern, namely, synchronisation, and how this is affected by different temporal resolutions and temporal heterogeneity. Twenty years of daily temperature data collected in 28 Dutch meteorological stations are used as case study. Given the complexity of the analysis, we propose a geovisual analytic approach based on self-organizing maps (SOMs). This approach allows exploring the data from two perspectives: (1) station-based, in which spatially synchronous weather stations are grouped into clusters; and (2) year-based, in which temporal synchronisation is analysed using a calendar year as basic unit and similar years are clustered. Clusters are identified using the SOM U-matrices and maps. Next, the spatial distribution of synchronous stations is displayed in the geographic space. Trend plots are used to illustrate trends in every cluster and the temperatures of stations and years are compared with the corresponding cluster representative values to identify anomalies in the temperature records. The analysis is repeated at daily, weekly and monthly resolutions to study the effects of different temporal resolutions on synchronisation. Also daily spatial synchronisation results for all years with those for groups of daily synchronous years are analysed to study the effects of temporal heterogeneity. Results show that synchronisation results are different at different temporal resolutions. Monthly results are the most stable ones both in station-based and year-based. It is also observed that spatial synchronisation results are simplified when considering temporal heterogeneity. Numéro de notice : A2013-464 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/1743277413Y.0000000067 En ligne : https://doi.org/10.1179/1743277413Y.0000000067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32602
in Cartographic journal (the) > vol 50 n° 3 (August 2013) . - pp 247 - 256[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 030-2013031 RAB Revue Centre de documentation En réserve L003 Disponible Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)
[article]
Titre : Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data Type de document : Article/Communication Auteurs : S. Khorram, Auteur ; H. Yuan, Auteur ; F. Van Der Wiele, Auteur Année de publication : 2011 Article en page(s) : pp 435 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multirésolution
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] données multicapteurs
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT
[Termes IGN] occupation du sol
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classificationRésumé : (Auteur) Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat Thematic Mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult. Numéro de notice : A2011-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.600462 Date de publication en ligne : 10/08/2011 En ligne : https://doi.org/10.1080/10106049.2011.600462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31181
in Geocarto international > vol 26 n° 6 (October 2011) . - pp 435 - 457[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2011061 RAB Revue Centre de documentation En réserve L003 Disponible Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map / A. Ehsani in Geoinformatica, vol 14 n° 4 (October 2010)
[article]
Titre : Effect of SRTM resolution on morphometric feature identification using neural network - self organizing map Type de document : Article/Communication Auteurs : A. Ehsani, Auteur ; F. Quiel, Auteur ; A. Malekian, Auteur Année de publication : 2010 Article en page(s) : pp 405 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aire protégée
[Termes IGN] Carpates
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] données topographiques
[Termes IGN] géomorphométrie
[Termes IGN] image SIR-C-X-SAR
[Termes IGN] MNS SRTMRésumé : (Auteur) In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 x 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 x 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis. Copyright Springer Numéro de notice : A2010-302 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-009-0085-4 Date de publication en ligne : 29/04/2009 En ligne : https://doi.org/10.1007/s10707-009-0085-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30496
in Geoinformatica > vol 14 n° 4 (October 2010) . - pp 405 - 424[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2010041 RAB Revue Centre de documentation En réserve L003 Disponible Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs / F. Giacco in IEEE Transactions on geoscience and remote sensing, vol 48 n° 10 (October 2010)
[article]
Titre : Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs Type de document : Article/Communication Auteurs : F. Giacco, Auteur ; C. Thiel, Auteur ; L. Pugliese, Auteur ; S. Scarpetta, Auteur ; M. Marinaro, Auteur Année de publication : 2010 Article en page(s) : pp 3769 - 3779 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de Kohonen
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] incertitude des donnéesRésumé : (Auteur) Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results. Numéro de notice : A2010-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2047863 Date de publication en ligne : 27/05/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2047863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30668
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 10 (October 2010) . - pp 3769 - 3779[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010101 RAB Revue Centre de documentation En réserve L003 Disponible Automatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)PermalinkCarto-Som: Cartogram creation using self-organizing maps / R. Henriques in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)PermalinkEarthquake-induced landslide hazard monitoring and assessment using SOM and PROMETHEE techniques: a case study at the Chiufenershan area in Central Taiwan / W.T. Lin in International journal of geographical information science IJGIS, vol 22 n° 8-9 (august 2008)PermalinkLand-cover classification using ASTER: multi-band combinations based on wavelet fusion and SOM neural network / H. Bagan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)PermalinkVisual analysis of network traffic – interactive monitoring, detection, and interpretation of security threats / Florian Mansmann (ca 2008)PermalinkA time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkDetecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps / Matthieu Molinier in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)PermalinkMesh simplification for building typification / Dirk Burghardt in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)PermalinkSubpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization / S. Lee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 6 (June 2006)PermalinkEvaluating the usability of visualization methods in an exploratory geovisualization environment / E.L. Koua in International journal of geographical information science IJGIS, vol 20 n° 4 (april 2006)Permalink