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Ontological considerations in GIScience / P. Agarwal in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 501 - 536 Titre : Ontological considerations in GIScience Type de document : Article/Communication Auteurs : P. Agarwal, Auteur Année de publication : 2005 Article en page(s) : pp 501 - 536 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] définition [Termes IGN] ontologie [Termes IGN] système d'information géographique [Termes IGN] terminologie Résumé : (Auteur) Ontology is a significant research theme in GIScience. While some researchers believe that the progress in GIScience is being directed through an engagement with the concept of ontology, some dismiss it as irrelevant. This paper is aimed at (i) exploring the theoretical and practical roles of ontologies; (ii) making the definitions and terminology explicit; (iii) assessing the applicability of ontology to problems in the geographical domain; and (iv) assessing whether a unified approach to ontology exists in GIScience. The results will be helpful for GIScientists in (i) understanding the validity of employing ontology within their own work, (ii) assessing what operational framework of terms and methods to use for developing their own ontology, and (iii) to assess what existing ontological models are available and applicable within their domain or application. A comprehensive and critical review will also help in identifying the significant issues and directing future research agenda in GIScience. Numéro de notice : A2005-235 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810500032321 En ligne : https://doi.org/10.1080/13658810500032321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27372 [article]

Exemplaires(2)

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079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An algebraic approach to automated geospatial information fusion / Matt Duckham in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 537 - 557 Titre : An algebraic approach to automated geospatial information fusion Type de document : Article/Communication Auteurs : Matt Duckham, Auteur ; Michael F. Worboys, Auteur Année de publication : 2005 Article en page(s) : pp 537 - 557 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] automatisation [Termes IGN] exploration de données géographiques [Termes IGN] fusion de données [Termes IGN] information géographique [Termes IGN] interopérabilité sémantique [Termes IGN] ontologie [Termes IGN] qualité des données [Termes IGN] raisonnement spatiotemporel Résumé : (Auteur) This paper presents a new technique for information fusion. Unlike most previous work on information fusion, this paper explores the use of instance-level (extensional) information within the fusion process. This paper proposes an algorithm that can be used automatically to infer the schema-level structure necessary for information fusion from instance-level information. The approach is illustrated using the example of geospatial land-cover data. The method is then extended to operate under uncertainty, such as in cases where the data are inaccurate or imprecise. The paper describes the implementation of the fusion method within a software prototype. Finally, the paper discusses several key topics for future research, including applications of this work to spatial-data mining and the semantic web. Numéro de notice : A2005-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810500032339 En ligne : https://doi.org/10.1080/13658810500032339 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27373 [article]

Exemplaires(2)

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079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Dynamic environmental modelling in GIS: 1. modelling in three spatial dimensions / D. Karssenberg in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 559 - 579 Titre : Dynamic environmental modelling in GIS: 1. modelling in three spatial dimensions Type de document : Article/Communication Auteurs : D. Karssenberg, Auteur ; K. de Jong, Auteur Année de publication : 2005 Article en page(s) : pp 559 - 579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] discrétisation spatiale [Termes IGN] langage de modélisation [Termes IGN] langage de programmation [Termes IGN] modèle dynamique [Termes IGN] modélisation 3D [Termes IGN] représentation du temps [Termes IGN] voxel Résumé : (Auteur) Environmental modelling languages are programming languages developed for building computer models simulating environmental processes. They come with database and visualization routines for the data used in the models. Environmental modelling languages provide the possibility to construct dynamic models, also called forward models, which are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a time step preceding t. Nowadays, these modelling languages can deal with simulations in two spatial dimensions, but existing software does not support the construction of models in three dimensions. We describe concepts of an environmental modelling language supporting dynamic model construction in two and three spatial dimensions. The lateral dimension is represented by gridded maps, with a regular discretization, while the vertical dimension is represented by an irregular discretization in voxels. Universal spatial functions are described with these entities of the modelling language as input. Dynamic modelling through time is possible by combining these functions in structured script sections, providing a section, which is executed repetitively, representing the time steps. The concepts of the language are illustrated with two example models, built with a prototype of the language. Numéro de notice : A2005-237 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810500032362 En ligne : https://doi.org/10.1080/13658810500032362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27374 [article]

Exemplaires(2)

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079-05051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Integration of genetic algorithms and GIS for optimal location search / X. Li in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 581 - 601 Titre : Integration of genetic algorithms and GIS for optimal location search Type de document : Article/Communication Auteurs : X. Li, Auteur ; A.G. Yeh, Auteur Année de publication : 2005 Article en page(s) : pp 581 - 601 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] algorithme génétique [Termes IGN] analyse combinatoire (maths) [Termes IGN] requête spatiale [Termes IGN] système d'information géographique Résumé : (Auteur) Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and muItiple objectives. The exhaustive blind (brute-force) search with high-dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value. Numéro de notice : A2005-238 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810500032388 En ligne : https://doi.org/10.1080/13658810500032388 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27375 [article]

Exemplaires(2)

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079-05051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Representing and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 603 - 621 Titre : Representing and reducing error in natural-resource classification using model combination Type de document : Article/Communication Auteurs : Zhi Huang, Auteur Année de publication : 2005 Article en page(s) : pp 603 - 621 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par arbre de décision [Termes IGN] classification par réseau neuronal [Termes IGN] erreur d'attribut [Termes IGN] erreur d'échantillon [Termes IGN] précision de la classification [Termes IGN] propagation d'erreur [Termes IGN] ressources naturelles Résumé : (Auteur) Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster-Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster-Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a 'majority voting system', a simple average, Dempster-Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction. Numéro de notice : A2005-239 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810500032446 En ligne : https://doi.org/10.1080/13658810500032446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27376 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
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079-05052	RAB	Revue	Centre de documentation	En réserve L003	Disponible