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Multidimensional Data Modeling for Feature Extraction and Mapping

Multidimensional Data Modeling for Feature Extraction and Mapping. E. Lynn Usery usery@usgs.gov. ACSM April 19, 2004. http://mcmcweb.er.usgs.gov/carto_research. Outline. Motivation Objectives Approach Theoretical Model Implementation Scale Dependent Feature Rendering

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Multidimensional Data Modeling for Feature Extraction and Mapping

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  1. Multidimensional Data Modeling for Feature Extraction and Mapping E. Lynn Usery usery@usgs.gov ACSM April 19, 2004 http://mcmcweb.er.usgs.gov/carto_research

  2. Outline • Motivation • Objectives • Approach • Theoretical Model • Implementation • Scale Dependent Feature Rendering • Conclusions

  3. Motivation • Conventional GIS model the world in two-dimensions with a map model and geographic features dependent on geometry for definition • This map model limits three-dimensional and temporal analysis, and multidimensional, multi-scale representations • Cognition studies indicate that humans perceive the geographic world as a set of definable entities with spatial, thematic, and temporal attributes associated

  4. Objectives • Provide a theoretical model based on feature orientation • Develop the model to support unique entities with spatial, thematic, and temporal attributes and relations for each feature instance • Implement the model in a feature library and use the library for feature extraction to support The National Map

  5. Approach • Implement the theoretical feature model in an object-oriented library • Develop feature instances for 20 specific features that are relevant to The National Map • Develop attributes and relationships including multiple representations (raster and vector) of attributes for each feature instance • Determine the extraction capability of each feature from various image sources

  6. Feature Model • Feature is geographic entity and object representation • One feature, many objects • Multiple resolutions • Multiple geometries • Access from single identity

  7. Definitions • Feature - A set of phenomena with common attributes and relationships. The concept of feature encompasses both entity and object. • Entity - A real-world phenomenon that cannot be subdivided into phenomena of the same kind. • Object - A digital representation of all or a part of an entity. • Attribute - Characteristic of a feature or of an attribute value. • Relationship - Linkage between features or objects. • Feature instance - An occurrence of a feature defined by a unique set of attributes and relationships.

  8. Databases Supporting Feature Extraction and Map Generation • Feature Attributes and Relationships • Image • Image Chips • Spectral Responses • Digital Number Ranges for Multimodal Images • Map • Symbol Specifications • Symbol Chips • Inclusion Criteria

  9. Feature Library Implementation

  10. Multiple Feature InstanceExample with Actual Data

  11. Feature Instance Implementation with Actual Water Quality Data

  12. Relationship Implementation from NHD

  13. Time Attribute Implementation

  14. National Map Feature Extraction • Camp Lejeune study site • 20 features selected • All attributes and relationships built based on DLG-E specifications • Image chips extracted for storage as attributes • Spectral responses determined (laboratory and from images)

  15. Table of the 20 Features

  16. Airport -- DOQ

  17. Airport – Ikonos Pan

  18. Airport – Ikonos Pan-sharpened

  19. Airport – Ikonos MX

  20. Airport – SPOT Pan

  21. Airport – CIR Photo

  22. Trail – DOQ

  23. Trail – Ikonos Pan

  24. Trail – Ikonos Pan-sharpened

  25. Trail – Ikonos MX

  26. Trail – SPOT

  27. Trail – CIR Photo

  28. Trail – Color Photo

  29. Airport Map Symbol

  30. Trail Map Symbol

  31. Geodatabase for the Study Area in ArcCatalog

  32. Airport Feature

  33. The Study Area – Camp Lejeune, NC

  34. Scale Dependent Renderer

  35. Trees on 1:30,000-Scale Map

  36. Trees on 1:9,000-Scale Map

  37. Buildings Rendered as Polygons 1:5,000-Scale Map

  38. Buildings Rendered as Polygons/Points Based on the Longest Axis -- 1:12,000-Scale Map

  39. Buildings Rendered as Points on 1:28,000-Scale Map

  40. Buildings Not Displayed on 1:55,000- Scale Map

  41. Conclusions • A theoretical model of features existing in the real world as single geographical entities has been developed • This model shows promise for implementing feature extraction methods and scale-dependent rendering for The National Map • Probabilities for extracting specific features from multimodal sources can be developed based on feature attributes and relationships and appearance in various image sources

  42. Multidimensional Data Modeling for Feature Extraction and Mapping E. Lynn Usery usery@usgs.gov ACSM April 19, 2004 http://mcmcweb.er.usgs.gov/carto_research

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