Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session F-2 WG II/3 - Technologies for Large-Volumes of Spatial Data - Part B]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: LARGE SPATIAL OBJECT HANDLING IN GEOGRAPHIC INFORMATION SYSTEMS Wenjin Zhou

Document type:: Monograph

Structure type:: Chapter

LARGE SPATIAL OBJECT HANDLING IN GEOGRAPHIC INFORMATION SYSTEMS Wenjin Zhou UNISYS SYSTEM 9 GIS 61 Middlefield Road Scarborough, Ontario, Canada M1S 5A9 ABSTRACT We have seen the increasing availability of large spatial objects in Geographic Information Systems(GISs) in the past two decades, mainly due to the interest of multi-media information systems. These large objects may come from remotely sensed imagery, scanned maps, digital terrain models, and simulation model output. Due to the large amount of data and rich semantics involved in the large objects, traditional methods used to handle text and graphics data will not work well for large spatial objects. Therefore how to effectively and efficiently store, manage and process large spatial object presents a special challenge to the current generation of GISs. This paper presents techniques that can be used to handle large spatial objects. After analyzing the nature of large spatial objects, we decompose them into three components: row data, Direct Related Attribute Data and Derived Attribute Data in order to make them more manageable in GISs. We then investigate GIS query patterns on large spatial objects and present an object oriented data model that uses objects, methods, rules, and class browser to handle large spatial objects in the context of GISs. We also present five abstraction techniques that can be implemented to effectively and efficiently support large objects in GISs. Finally we use several practical examples to demonstrate the effectiveness of the suggested methods. We conclude the paper with a short discussion of system performance and further research issues. KEY WORDS: Large spatial object, Object oriented model, Data base, Query Patterns, Abstraction, GIS Landsat Image Class Digi Imagery. image_name: string a" Ÿ resolution: array[double] : origin: point Satellite Air Photo Video size: array[integer] Y N plane_no: integer time: date Single Band Multi Bands quality: integer footprint: polygon Sa icon: pixelmap Color Multi-Spectrum Figure 1 (a): Class Browser Figure 1 (b): Class Hierarchy Browser 212 In the pe has been spatial c terrain m interest informat informat roughly category two Or 1 processir Base Ma image ok can be | Wegener category systems | oriented systems Deux [19 However in GISs : systems : image indepenc the DBM limits t managen unnecess because method, data str abstracti inefficier data set 1 together interacti In this p: highligh: object ori digital investig: Several r amount efficiency Support practical perform: techniqu discussic research

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