| Summary: | 碩士 === 國立成功大學 === 測量工程學系 === 85 === Abstract
Techniques of GIS data acquisition have been steadily improved
over the past years, therefore, the variety and volume of
accumulated GIS data also grow very fast in the same period
of time. How to efficiently manage these volumes of complex
data and effectively take advantage of their existence
become a bottleneck for the future GIS development. Past
research demonstratedthat the accuracy of image classification
can be improved by introducing ancillary information like
landcover or landuse data. Since GISs are capable of storing
these kinds of information, a further integration of GIS and
RemoteSensing technologies will bring a strong impact on the
development of both technologies.
This research focuses on the data selection process in the
geographic information system environment. We suggest a
knowledge-based approach, which replace the human users''
interpretation and judgment with a knowledge base loaded with
rules about human spatial and domain knowledge. The system is
designed to understand users'' requirement, infer their
requirement with the understanding about GIS data, and then
transform the requirement to a query executable with GIS DBMS.
It is expected to solve the semantic gap about reality
phenomena due to the different definitions from application
domains (e.g., remote sensing) and existing GISs. Under such
circumstance, users no longer need to have a thorough
understanding before accessing ancillary information from
GISs and the working load is transferred to the system.
This research started with a general analysis about building a
KB based on theunderstanding of GIS data, we then use remote
sensing image classification as examples to demonstrate how a
knowledge-based system can help choosing appropriate
ancillary information to meet certain domain requirements.
The result about integrating knowledge bases with GISs to aid
information selection is very appealing. With careful selection
and formalization of spatial and domain knowledge, it is
optimistic to expect GISs to be able to meet more domain needs
in the future.
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