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An artificial-neural-network-based, constrained CA model for simulating urban growth / Q. Guan in Cartography and Geographic Information Science, vol 32 n° 4 (October 2005)
[article]
Titre : An artificial-neural-network-based, constrained CA model for simulating urban growth Type de document : Article/Communication Auteurs : Q. Guan, Auteur ; L. Wang, Auteur ; K.C. Clarke, Auteur Année de publication : 2005 Article en page(s) : pp 369 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] automate cellulaire
[Termes IGN] croissance urbaine
[Termes IGN] données localisées
[Termes IGN] modèle mathématique
[Termes IGN] occupation du sol
[Termes IGN] Pékin (Chine)
[Termes IGN] réseau neuronal artificiel
[Termes IGN] simulationRésumé : (Auteur) Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and drus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network bas been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand to urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001-2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty. Numéro de notice : A2005-539 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/152304005775194746 En ligne : https://doi.org/10.1559/152304005775194746 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27675
in Cartography and Geographic Information Science > vol 32 n° 4 (October 2005) . - pp 369 - 380[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-05041 RAB Revue Centre de documentation En réserve L003 Disponible Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling / F. Samadzadegan in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 5 (August - October 2005)
[article]
Titre : Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling Type de document : Article/Communication Auteurs : F. Samadzadegan, Auteur ; A. Azizi, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 255 - 277 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] Allemagne
[Termes IGN] image aérienne
[Termes IGN] image en couleur
[Termes IGN] milieu urbain
[Termes IGN] raisonnement flou
[Termes IGN] reconnaissance automatique
[Termes IGN] reconnaissance d'objets
[Termes IGN] reconstruction 3D
[Termes IGN] réseau neuronal artificiel
[Termes IGN] visualisation 3DRésumé : (Auteur) Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper, a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany. Copyright ISPRS Numéro de notice : A2005-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.02.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.02.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27487
in ISPRS Journal of photogrammetry and remote sensing > vol 59 n° 5 (August - October 2005) . - pp 255 - 277[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-05031 SL Revue Centre de documentation Revues en salle 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)
[article]
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 IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] base de connaissances
[Termes IGN] carte cognitive
[Termes IGN] carte de Kohonen
[Termes IGN] congruence
[Termes IGN] représentation cartographique
[Termes IGN] représentation mentale spatiale
[Termes IGN] réseau neuronal artificiel
[Termes IGN] simulationRésumé : (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 DOI : 10.1559/1523040054738963 En ligne : https://doi.org/10.1559/1523040054738963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27553
in Cartography and Geographic Information Science > vol 32 n° 3 (July 2005) . - pp 161 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-05031 RAB Revue Centre de documentation En réserve L003 Disponible Visualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)
[article]
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 : [Termes IGN] analyse diachronique
[Termes IGN] analyse spatiale
[Termes IGN] carte de Kohonen
[Termes IGN] données démographiques
[Termes IGN] données multidimensionnelles
[Termes IGN] données socio-économiques
[Termes IGN] graphe
[Termes IGN] noeud
[Termes IGN] représentation spatiale
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] visualisation de données
[Vedettes matières IGN] GéovisualisationRésumé : (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 DOI : 10.1007/s10707-005-6670-2 En ligne : https://doi.org/10.1007/s10707-005-6670-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27362
in Geoinformatica > vol 9 n° 2 (June - August 2005) . - pp 159 - 179[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-05021 RAB Revue Centre de documentation En réserve L003 Disponible Neural network model for standard PCA and its variants applied to remote sensing / S. Chitroub in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)
[article]
Titre : Neural network model for standard PCA and its variants applied to remote sensing Type de document : Article/Communication Auteurs : S. Chitroub, Auteur Année de publication : 2005 Article en page(s) : pp 2197 - 2218 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] extraction automatique
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] matrice de covariance
[Termes IGN] modèle topologique réseau
[Termes IGN] réseau neuronal artificiel
[Termes IGN] valeur propreRésumé : (Auteur) The conventional approach for principal component analysis (PCA) and its variants applied to remote sensing involves the computation of the input data covariance/correlation matrix and/or that of noise and application of diagonalization procedures for extracting the eigenvalues and corresponding eigenvectors. When the data dimension grows significantly, the matrix computations and manipulations become practically inefficient and inaccurate due to round-off errors. In addition, all the eigenvalues and their corresponding eigenvectors have to be evaluated. These deficiencies make the conventional scheme inefficient for remote sensing applications. For that we propose here a neural network model that performs the PCA and its variants directly from the original data without any additional non-neuronal computations or preliminary matrix estimation. Since the end user is usually not a neural network specialist, the neural network model as well as its execution are carefully designed in order to be automated as much as possible. This includes both the design of the network topology and the input/output representation as well as the design of the training algorithms. The global convergence of the model is studied. Its application has been realized on Landsat Thematic Mapper (TM) multispectral data. The obtained results show that the model performs well. Numéro de notice : A2005-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500075899 En ligne : https://doi.org/10.1080/01431160500075899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27396
in International Journal of Remote Sensing IJRS > vol 26 n° 10 (May 2005) . - pp 2197 - 2218[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-05101 RAB Revue Centre de documentation En réserve L003 Disponible Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)PermalinkVicarious radiometric calibration of satellite ocean colour sensors / D. Antoine (01/09/2004)PermalinkA split model for extraction of subpixel impervious surface information / Y. Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 7 (July 2004)PermalinkArtificial neural network-based techniques for the retrieval of SWE [snow water equivalent] and snow depth from SSM/I data / Marco Tedesco in Remote sensing of environment, vol 90 n° 1 (15/03/2004)PermalinkIntegrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa / Onisimo Mutanga in Remote sensing of environment, vol 90 n° 1 (15/03/2004)PermalinkA hybrid texture segmentation method for mapping urban land use / Nezamoddin N. Kachouie in Geomatica, vol 58 n° 1 (March 2004)PermalinkAn artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas / M.K. Arora in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)PermalinkToward universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements / G. Le Maire in Remote sensing of environment, vol 89 n° 1 (15/01/2004)PermalinkModeling reality: how computers mirror life / Iwo Bialynicki-Birula (2004)PermalinkClassification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks / C.S. Murthy in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkTraining a neural network with a canopy reflectance model to estimate crop leaf area index / F. Mark Danson in International Journal of Remote Sensing IJRS, vol 24 n° 23 (December 2003)PermalinkA cognitive pyramid for contextual classification of remote sensing images / E. Binaghi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)PermalinkKnowledge discovery from soil maps using inductive learning / F. Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)PermalinkA neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery / E. Binaghi in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkSimulation of development alternatives using neural networks, cellular automata, and GIS for urban planning / A.G. Yeh in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkWater quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland / Y. Zhang in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)PermalinkLe boosting : une méthode de classification non paramétrique / Michel Arnaud in Revue internationale de géomatique, vol 12 n° 4 (décembre 2002 – février 2003)PermalinkCalibration of stochastic cellular automata: the application to rural-urban land conversions / F. Wu in International journal of geographical information science IJGIS, vol 16 n° 8 (december 2002)PermalinkNeural-network-based cellular automata for simulating multiple land use changes using GIS / X. Li in International journal of geographical information science IJGIS, vol 16 n° 4 (june 2002)PermalinkArtificial neural networks as a method of spatial interpolation for digital elevation models / D.A. Merwin in Cartography and Geographic Information Science, vol 29 n° 2 (April 2002)Permalink