Centre dedocumentation
scientifique

Amalgamation in cartographic generalization using Kohonen's feature nets / M.K. Allouche in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 8 - 9 (september 2005) . - pp 899 - 914 Titre : Amalgamation in cartographic generalization using Kohonen's feature nets Type de document : Article/Communication Auteurs : M.K. Allouche, Auteur ; Bruno Moulin, Auteur Année de publication : 2005 Article en page(s) : pp 899 - 914 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] déformation géométrique [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données multiéchelles [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] triangulation de Delaunay [Vedettes matières IGN] Généralisation Résumé : (Auteur) Empirical observations of the way cartographers deal with generalization problems lead to the hypothesis that they first detect patterns of anomalies in the cartographic data set and then eliminate anomalies by transforming the data. Automatically identifying patterns of anomalies on the map is a difficult task when using GIS functions or traditional algorithmic approaches. Techniques based on the use of neural networks have been widely used in artificial intelligence in order to solve pattern-recognition problems. In this paper, we explore how Kohonen-type neural networks can be used to deal with map generalization applications in which the main problem is to identify high-density regions that include cartographic elements of the same type. We also propose an algorithm to replace cartographic elements located in a region by its surrounding polygon. The use of this type of neural network permitted us to generate different levels of grouping according to the chosen zoom-scale on the map. These levels correspond to a multiple representation of the generalized cartographic elements. As an illustration, we apply our approach to the automatic replacement of a group of houses represented as a set of very close points in the original data set, by a polygon representing the corresponding urban area in the generalized map. Numéro de notice : A2005-406 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27542 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-05081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
079-05082	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Optimization approaches for generalization and data abstraction / Monika Sester in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 8 - 9 (september 2005) . - pp 871 - 897 Titre : Optimization approaches for generalization and data abstraction Type de document : Article/Communication Auteurs : Monika Sester, Auteur Année de publication : 2005 Article en page(s) : pp 871 - 897 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] déplacement d'objet géographique [Termes descripteurs IGN] données localisées numériques [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] représentation des détails topographiques [Termes descripteurs IGN] typification [Vedettes matières IGN] Généralisation Résumé : (Auteur) The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least-Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self-Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales. Numéro de notice : A2005-405 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27541 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-05081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
079-05082	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

A statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 8 (August 2005) . - pp 1890 - 1900 Titre : A statistical self-organizing learning system for remote sensing classification Type de document : Article/Communication Auteurs : H.M. Chi, Auteur ; O.K. Ersoy, Auteur Année de publication : 2005 Article en page(s) : pp 1890 - 1900 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] noeud [Termes descripteurs IGN] système expert [Termes descripteurs IGN] transformation non linéaire Résumé : (Auteur) A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality. Numéro de notice : A2005-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27529 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05081	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Assessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps / R.E. Lloyd in Cartography and Geographic Information Science, vol 32 n° 3 (July 2005)

Public [article]  inCartography and Geographic Information Science > vol 32 n° 3 (July 2005) . - pp 161 - 179 Titre : Assessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps Type de document : Article/Communication Auteurs : R.E. Lloyd, Auteur Année de publication : 2005 Article en page(s) : pp 161 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie numérique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] base de connaissances [Termes descripteurs IGN] carte cognitive [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] congruence [Termes descripteurs IGN] représentation cartographique [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] simulation Résumé : (Auteur) Real cognitive maps encoded by humans are difficult to study using experimental methods because they are a product of complex processes whose content and timing, cannot easily be known or controlled. This paper assesses the value of using neural network model simulations for investigating cognitive maps. The study simulated the learning of mapped city locations in South Carolina from reference sites in the three primary regions of the state using Kohonen selforganizing maps. The learning performances of models were considered based on available prior knowledge. Bi-dimensional regression analyses were used to assess the congruity of the simulated cognitive maps with a cartographic map and with sketch maps produced by human subjects. Error analyses indicated differences between central and peripheral reference sites. The cities known by subjects living at a central location were more evenly distributed in space and associated with significantly smaller errors. Models that learned combined state boundary and interstate highway information as prior knowledge or simultaneously with city locations consistently produced the best simulation results. The results indicated simulated cognitive maps could be used effectively to study the acquisition of spatial knowledge. Numéro de notice : A2005-417 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27553 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-05031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
032-05032	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Visualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)

Public [article]  inGeoinformatica > vol 9 n° 2 (June - August 2005) . - pp 159 - 179 Titre : Visualizing demographic trajectories with self-organizing maps Type de document : Article/Communication Auteurs : A. Skupin, Auteur ; R. Hagelman, Auteur Année de publication : 2005 Article en page(s) : pp 159 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] carte de Kohonen [Termes descripteurs IGN] données multidimensionnelles [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] graphe [Termes descripteurs IGN] noeud [Termes descripteurs IGN] représentation spatiale [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] visualisation de données Résumé : (Auteur) In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the magnitude of change occurring between different time periods. This paper proposes to add another perspective to the visualization of temporal change, by linking multi-temporal observations to a geometric configuration that is not based on geographic space, but on a spatialized representation of n-dimensional attribute space. The presented methodology aims at providing a cognitively plausible representation of changes occurring inside census areas by representing their attribute space trajectories as line features traversing a two-dimensional display space. First, the self-organizing map (SOM) method is used to transform n-dimensional data such that the resulting two-dimensional configuration can be represented with standard GIS data structures. Then, individual census observations are mapped onto the neural network and linked as temporal vertices to represent attribute space trajectories as directed graphs. This method is demonstrated for a data set containing 254 counties and 32 demographic variables. Various transformations and visual results are presented and discussed in the paper, from the visualization of individual component planes and trajectory clusters to the mapping of different attributes onto temporal trajectories. Numéro de notice : A2005-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27362 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-05021	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Designing rule based classifier using self-organizing feature map for analysis of multispectral satellite images / N.R. Pal in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)

Permalink

Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach / D. Guo in Cartography and Geographic Information Science, vol 32 n° 2 (April 2005)

Permalink

Estimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR / Grégoire Mercier in IEEE Transactions on geoscience and remote sensing, vol 43 n° 2 (February 2005)

Permalink

Using maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) / Hassan A. Karimi in Geocarto international, vol 19 n° 1 (March - May 2004)  

Permalink

Agile 2004, 7th Agile Conference on Geographic Information Science, Heraklion (Greece), 29 April - 1 May 2004 / Fred Toppen (2004)

Permalink

Généralisation et représentation multiple / Anne Ruas (2002)

Permalink