1. Summary

The aim of the "Digital Typhoon" project is to be a good example as the application of meteoinformatics to large-scale real-world issues. Hence the two big challenges of this project are (1) to build, for the typhoon image collection, a large-scale scientific databases which are the foundation of meteoinformatics, (2) to establish algorithms and database models for the discovery of information and knowledge useful for typhoon analysis and prediction.

2. Purpose

Poster
  1. Collect as many typhoon data as possible from satellite observations to establish the comprehensive archives of typhoon data.
  2. Apply various informatics-based approaches (meteoinformatics) such as pattern recognition, computer vision, data mining, to typhoon image collection as a real world large-scale application, and investigate new frameworks and developments to spatio-temporal data mining techniques.
  3. Discover new methods and knowledge relevant for typhoon analysis and prediction from a different viewpoint in comparison to a meteorology perspective, and moreover use those new techniques for the socially relevant problems such as early warning, or the discovery of the precursors.

3. Major Research Challenges

Typhoon Image Collection

Collect satellite typhoon images that are consistently high quality and cleaned throughout the image collection, and also appropriately tailored for meteorological applications.

Data Mining

Discover new knowledge from the collection of typhoon data archives. This includes traditional data mining approaches, but the chaotic nature of the atmosphere poses challenge to those approaches. More interesting approach is to find or predict a rare but important event from typhoon cloud patterns.

Mathematical Representation of Typhoon Cloud Patterns

Represent typhoon cloud patterns in a quantitative way. This method should represent complex cloud patterns that change rapidly along time.

Typhoon Multimedia Database Systems

To make the most of the typhoon image databases of several tens of thousand images created from typhoon image collection, we have to create solutions to the following list of challenges.

4. Demonstration

  1. Digital Typhoon (Typhoon Image Database)

5. References (Complete List)

  1. Asanobu KITAMOTO, "Content Management Systems for Information Aggregation on Emergency Events such as Natural Disasters", The 19th Annual Conference of the Japanese Society for Artificial Intelligence, No. 3C3-02, 2005-06 (in Japanese) [ Abstract ] [ PDF ]
  2. Asanobu KITAMOTO, "Digital Typhoon: Near Real-Time Aggregation, Recombination and Delivery of Typhoon-Related Information", Proceedings of the 4th International Symposium on Digital Earth (ISDE), pp. (CD-ROM), 2005-03 [ Abstract ] [ PDF ]
  3. Asanobu KITAMOTO, "Chance Discovery in Natural Phenomena", Information Technology for Chance Discovery -- Decision Making Support in the Era of Post Data Mining, Y. OSAWA (Eds.), pp. 43-56, Tokyo Denki University Press, ISBN 4-501-53640-3, 2003-09 (in Japanese) [ Abstract ]
  4. Asanobu KITAMOTO, "Data Mining from the Multiple Alignment of Typhoon Image Sequences", Technical Report of IEICE (Institute of Electronics, Information, and Communication Engineers), Vol. PRMU2002-159, pp. 79-84, 2002-12 (in Japanese) [ Abstract ] [ PDF ]
  5. Asanobu KITAMOTO, "Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-temporal Clustering", Discovery Science (DS2002), Lecture Notes in Computer Science (LNCS) 2534, Lange, S., Satoh, K., and Smith, C.H. (Eds.), pp. 283-290, Springer, doi:10.1007/3-540-36182-0_26, 2002-11 [ Abstract ]
  6. Asanobu KITAMOTO, "IMET: Image Mining Environment for Typhoon Analysis and Prediction", Multimedia Mining, Djeraba, C. (Eds.), pp. 7-24, Kluwer Academic Publishers, ISBN 1-4020-7247-3, 2002-11 [ Abstract ]
  7. Asanobu KITAMOTO, "Spatio-temporal Data Mining for Typhoon Image Collection", Journal of Intelligent Information Systems, Vol. 19, No. 1, pp. 25-41, doi:10.1023/A:1015560319636, 2002-07 [ Abstract ]
  8. Asanobu KITAMOTO, "Typhoon Analysis and Data Mining with Kernel Methods", Pattern Recognition with Support Vector Machines (SVM2002), Lecture Notes in Computer Science (LNCS) 2388, Lee, S.W., and Verri, A. (Eds.), pp. 237-248, Springer, doi:10.1007/3-540-45665-1_18, 2002-08 [ Abstract ]
  9. Asanobu KITAMOTO, Kinji ONO, "The Collection of Typhoon Data and the Construction of Typhoon Image Databases Under International Research Collaboration between Japan and Thailand", NII Journal, No. 2, pp. 15-26, 2001-03 [ Abstract ] [ PDF ]
  10. Asanobu KITAMOTO, Kinji ONO, "The Construction of Typhoon Image Collection and its Application to Typhoon Analysis", NII Journal, No. 1, pp. 7-22, 2000-12 (in Japanese) [ Abstract ] [ PDF ]
  11. Asanobu KITAMOTO, "Interpretation of Typhoon Cloud Patterns by Holistic Analysis", Technical Report of IEICE (Institute of Electronics, Information, and Communication Engineers), Vol. PRMU2000-240, pp. 129-136, 2001-03 (in Japanese) [ Abstract ] [ PDF ]