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Sinopsis
This proceedings of a NATO Advanced Research Workshop (ARW) presents latest interdisciplinary research results on spatial data structures, mapping systems, cartographic feature extraction from imagery, mobile robot navigation, and operational and researchneeds in mapping. The emphasis is on specific techniques for map production and use.
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Reseña del editor
The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.
Reseña del editor
This volume presents the proceedings of a NATO Advanced Research Workshop (ARW) held in Denmark in August 1989. It results from the activities of the NATO Special Programme on Sensory Systems for Robotic Control, running since 1983 under the auspices of the NATO Science Committee. The volume collects up-to-date interdisciplinary research findings of high practical value, about spatial- data structures, mapping systems, cartographic feature extraction from imagery, mobile robot navigation, and operational and research needs in mapping. As the demand for mapping products and services is growing very rapidly, but the transition to digital map production is still slow, the emphasis in this volume is on specific techniques for map production and their use in various navigation tasks. The key disciplines offering new contributions here are image processing, computational geometry, artificial intelligence, hypermedia, geography, sensor fusion, route planning, and computational environments.
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Mapping and Spatial Modelling for Navigation
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Springer Berlin Heidelberg, 2012
ISBN 10: 3642842178
ISBN 13: 9783642842177
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proceedings of the NATO Advanced Research Workshop on Mapping and Spatial Modelling for Navigation, held in Fano, Denmark, August 21-25, 1989The successful implementation of applications in spatial reasoning requires paying attention to the representati. Nº de ref. del artículo: 5072039
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Mapping and Spatial Modelling for Navigation
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Springer Berlin Heidelberg, 2012
ISBN 10: 3642842178
ISBN 13: 9783642842177
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form 'find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. ' Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj. Nº de ref. del artículo: 9783642842177
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Mapping and Spatial Modelling for Navigation: (closed)) (NATO ASI Subseries F:)
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Mapping and Spatial Modelling for Navigation
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Springer Berlin Heidelberg Jan 2012, 2012
ISBN 10: 3642842178
ISBN 13: 9783642842177
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form 'find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. ' Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj. 372 pp. Englisch. Nº de ref. del artículo: 9783642842177
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Mapping and Spatial Modelling for Navigation: (closed)) (NATO ASI Subseries F:)
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Mapping and Spatial Modelling for Navigation
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Jan 2012, 2012
ISBN 10: 3642842178
ISBN 13: 9783642842177
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form 'find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. ' Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch. Nº de ref. del artículo: 9783642842177
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Mapping and Spatial Modelling for Navigation
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Mapping and Spatial Modelling for Navigation
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Condición: New. Print on Demand pp. viii + 357 155 Figures. Nº de ref. del artículo: 55079494
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Mapping and Spatial Modelling for Navigation
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Condición: New. PRINT ON DEMAND pp. viii + 357. Nº de ref. del artículo: 1854513043
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Mapping and Spatial Modelling for Navigation (Nato Asi Series (Closed) / Nato Asi Subseries F: (Closed))
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Paperback. Condición: Brand New. reprint edition. 365 pages. 9.40x6.80x0.90 inches. In Stock. Nº de ref. del artículo: x-3642842178
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