Artificial intelligence and geographic information systems: mutual meeting and passing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F09%3A00010659" target="_blank" >RIV/61989592:15310/09:00010659 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Artificial intelligence and geographic information systems: mutual meeting and passing

Popis výsledku v původním jazyce

Complicated systems such as geographic information systems (GIS) require sophisticated reasoning mechanisms for dealing with spatial information maintained by techniques in database management systems. However, their usage is limited, especially for thesystems where data are very dynamic and need intelligent decision making support in the presence of some uncertainty. In last years, many geoscientific researchers have begun to examine non-parametric techniques that may explain and model environmental data (Bradshaw et al. 1999; Chon et al. 1996; Ozesmi and Ozesmi 1999). It has been suggested that neural networks and case-based reasoning may provide appropriate techniques for ecological modelling (Bradshaw et al. 1999; Holt and Benwell 1999). Due to the inherent spatial nature of geographical data, it seems reasonable that such modelling be done within a GIS. In the last ten years there has been a significant increase in the application of artificial intelligence (AI) to many practical

Název v anglickém jazyce

Artificial intelligence and geographic information systems: mutual meeting and passing

Popis výsledku anglicky

Complicated systems such as geographic information systems (GIS) require sophisticated reasoning mechanisms for dealing with spatial information maintained by techniques in database management systems. However, their usage is limited, especially for thesystems where data are very dynamic and need intelligent decision making support in the presence of some uncertainty. In last years, many geoscientific researchers have begun to examine non-parametric techniques that may explain and model environmental data (Bradshaw et al. 1999; Chon et al. 1996; Ozesmi and Ozesmi 1999). It has been suggested that neural networks and case-based reasoning may provide appropriate techniques for ecological modelling (Bradshaw et al. 1999; Holt and Benwell 1999). Due to the inherent spatial nature of geographical data, it seems reasonable that such modelling be done within a GIS. In the last ten years there has been a significant increase in the application of artificial intelligence (AI) to many practical

Druh

D - Stať ve sborníku

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

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Projekt

<a href="/cs/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Metody umělé inteligence v GIS</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on Intelligent Networking and Collaborative Systems INCos 2009

ISBN

978-0-7695-3858-7

ISSN

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e-ISSN

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Počet stran výsledku

421

Strana od-do

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Název nakladatele

IEEE Computer Society Press

Místo vydání

New York

Místo konání akce

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Datum konání akce

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Typ akce podle státní příslušnosti

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Kód UT WoS článku

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