| Project/Area Number |
18500114
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kochi University |
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Principal Investigator |
HONDA Rie Kochi University, Faculty of Science, Associate Professor (80253334)
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| Co-Investigator(Kenkyū-buntansha) |
KIKUCHI Tokio Kochi University, Faculty of Science, Professor (70127926)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,870,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥270,000)
Fiscal Year 2007: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2006: ¥2,700,000 (Direct Cost: ¥2,700,000)
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| Keywords | machine learning / artificial intelligence / prediction / remote sensing / Earth Environment |
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| Research Abstract |
The approach of data mining is effective to explore of a large scale database and to support discovery of the knowledge from it. Recently, the targets of data mining have been changed from a simple tabular data to more complex data such as graph structure. Among them, mining of the spatio-temporal data is one of the most promising fields, because spatio-temporal data occurs very often in the scientific, engineering data and the real world data in a large scale.
In this study, the framework of spatio-temporal data mining was examined by using the Earth observation satellite data as the test bed. We used the weather satellite images stored at Kochi University for more than 10 years and the Global model of vegetation indices data for more than 22 years, which are created from U. S. satellite NOAA series satellites (GIMMS) . Both data are well calibrated and maintained.
As a result of three-year studies, some important process of the spatio-temporal data mining were implemented and examined by using the real data: (1) semi-automatic object extraction and tracking and its application to clouds in the weather satellite imagery, (2) statistical modeling of vegetation index for NDVI, (3) spatio-temporal correlation analysis and (4) visualization of obtained pattern in the 3D spatio temporal space and the interactive visual data mining.
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