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technologies are needed to easily and selectively access the information content of image archives and finally to increase the actual exploitation of satellite observations. [Datcu, et. al.]
Data mining, also known as knowledge discovery from databases, is the higher-level process of obtaining information through distilling information into knowledge ideas and beliefs about the mini-world through interpretation of
information and integration with existing knowledge. Data mining is concerned with investigators formulating new predictions and hypotheses from data as opposed to testing deductions from theories through a sub-process of
induction from a scientific database.
Data mining uses an IG_KnoweledgeBase as a repository that integrates IG_Image from one or more sources. In contrast to transactional database design, good IG_KnoweledgeBase design maximizes the efficiency of analytical
data processing or data examination for decision making. In addition to data mining, a IG_KnoweledgeBase often supports online analytical processing OLAP tools. OLAP tools provide multidimensional summary views of the
IG_KnoweledgeBase, e.g., roll-up increasing the level of aggregation, drill-down decreasing the level of aggregation, slice and dice selection and projection and pivot re-orientation of the multidimensional data view.
As a specialization of data mining for broader information, geospatial specifics are utilized. Examples include: geographic measurement frameworks geometry and topology; spatial dependency and heterogeneity; complexity of
spatio-temporal objects and rules; and diverse datatypes for geographic imagery.
Editors note: an item for IT Roadmap “Languages to describe data mining patterns; Describe patterns to be found and those found.” Relate to ISO 19109 Feature Cataloguing?
8.4.3.4 Feature fusion
Fusion is the process of combine remote sensing data with other sources of geospatial information to improve the understanding of specific phenomena.
8.5 Geographic imagery for decisions – application context
8.5.1 Decision Context IG_Context
Ed note: discuss common operating picture Imagery is useful to a specific application context if the geometric and attribute values are appropriate to the context.
For example the spatial resolution must be appropriate to the mapping scale in the application. An example is to use imagery as a base map in which case the imagery must have:
o a set of natural colors, directly interpretable by non-specific users, and uniform radiometry
o reach accurate geometrical details at least one meter resolution.
To be used as an information source, the satellite image will have : o
to be available as a value added product allowing an updating at least annual, available “on hand”, according to a very flexible limit, at reasonable cost ;
o to cover large territories in order to lay out the most uniform possible radiometry and thus to overcome one of
the problems encountered on orthophotographs.
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Table 13 – Applications and spatial resolution Scales of applications in urban areas
Image data used for these Applications Applications
Scales Images
Resolution
Technical management
1:200 to 1:500 Orthophotograph
20 cm Basic mapping
1:1000 to 1:2000 Orthophotograph
20 to 50 cm Urban planning
1:5000 to 1:10000 Orthophotograph
50 cm to 1 m Prospective
1:10000 to 1:1000000
SPOT P and XS Landsat
10 to 30 m
Figure 17 - Applications based on resolution
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Figure 18 - Imagery requirements for selected applications
Table 14 provides a taxonomy of geographic application areas. The categories are orthogonal, i.e., non-overlapping although some existing applications may be in more than one category. The number of categories is manageable,
i.e., 7 +- 2.
A decision tree for selecting an Application Area is provided in an Annex.
8.5.2 Decision fusion
Combine remote sensing data with other sources of geospatial information to improve the understanding of specific phenomena.
Fusion Levels: I-GRSS reference
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Table 14 - Application area taxonomy
Societal Surveillance Defense and Intelligence
Law Enforcement History or Archaeology Research
Societal Infrastructure Electric and Gas Utilities
Telecommunications Transportation including Aviation and Aerospace
Societal Commerce Business Site Determination
Architecture Engineering and Construction
Natural Resource Stewardship Earth, Ocean, or Atmospheric Research
Health Care Ecology and Conservation
Pollution Monitoring and Control
Natural Resource Exploitation Agriculture
Mining and Petroleum Forestry and Lumber
Fisheries and Marine Resource Use Water Distribution and Resources
Waste Disposal and Management
Societal Impact Reduction Emergency Management
Property Insurance
Education K-12 Education
University Education Museums
Public Consumers Tourism
Real Estate Entertainment
Journalism Employment Services
8.5.3 Visualization 8.5.3.1 Human
