Japan Geoscience Union Meeting 2015

Presentation information


Symbol M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI37] Earth and planetary informatics with huge data management

Thu. May 28, 2015 11:00 AM - 12:45 PM 203 (2F)

Convener:*Eizi TOYODA(Numerical Prediction Division, Japan Meteorological Agency), Mayumi Wakabayashi(Kiso-Jiban Consultants Co.,Ltd), Susumu Nonogaki(Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology), Eizi TOYODA(Numerical Prediction Division, Japan Meteorological Agency), Ken T. Murata(National Institute of Information and Communications Technology), Junya Terazono(The University of Aizu), Tomoaki Hori(Nagoya University Solar Terrestrial Environment Laboratory Geospace Research Center), Kazuo Ohtake(Japan Meteorological Agency), Takeshi Horinouchi(Faculty of Environmental Earth Science, Hokkaido University), Chair:Eizi TOYODA(Numerical Prediction Division, Japan Meteorological Agency), Mayumi Wakabayashi(Kiso-Jiban Consultants Co.,Ltd)

11:45 AM - 12:00 PM

[MGI37-20] Extraction of moving object from spatio-temporal data and modeling of its generation extinction process

*Rie HONDA1, Tomoya MATSUNAGA1, Keita MORI1, Ken T. MURATA2, Yoshiaki NAGAYA2, Kentaro UKAWA3 (1.Kochi University, 2.National Institute of Information and Communications Technology, 3.Systems Engineering Consultants Co., LTD.)

Keywords:Spatio-temporal, data mining, objects, modeling, weather images, radar

A large amount of spatio-temporal data has been accumulated in the various field of the Earth science such as weather satellite observation and ground radar observation. Moving objects are often included in these spatio-temporal data. For example, the cloud lumps in the weather image, the rainfall area in radar data are equivalent to objects. These objects are generated at some time point, survive with their shape and feature changed for a while, and finally extinct. Objects interact each other, sometimes are fused or decomposed. The basic information of these objects are the position and the shape of these objects, feature based on texture or spatial pattern. We developed the method to extract theses information from spatio-temporal data semi-automatically in order to find higher-order spatio-temporal variation pattern from them. The objects are modeled by the combination of multivariate normal distributions and its model parameters are determined via EM algorithm. The number of components was determined based on BIC.
The developed method is applied to cloud lump extraction from a weather satellite image (IR1 image of MTSAT 6 and 7) and the extraction of the rainfall area from 3 dimensional weather radar data.