Knowledge Sharing Symposium on Machine Learning and Deep Learning in Geoinformatics (online)
- オフィス GLP
- 2020年11月29日
- 読了時間: 1分
Date:
30 November – 3 December 2020, Hokkaido University, Japan
Mode:
Online using ZOOM
Fee:
Registration is Free!
Aim:
The purpose of this symposium is to organize one day knowledge sharing technical session to discuss applications of machine learning (ML) and deep learning (DL) in geospatial data for environmental monitoring, followed by three days of hands-on training. The first day of the symposium is fully dedicated to bringing together the researchers and scientists to discuss and share knowledge about the recent development in ML and DP applications in Geoinformatics. This symposium will provide an opportunity to share knowledge, facilitate discussions, and encourage research collaborations in the field of geospatial applications. This symposium will be useful for expanding collaborative research among the participants and discussion on future funding opportunities. The symposium will be followed by a three days hands-on-training to graduate students to learn about ML and DL. This symposium will provide an opportunity to expand the network and research collaborative in the future.
Program
Day1
Day2-3
Organizer:
Hokkaido University and Global Land Programme (GLP) Japan Nodal Office
Organizing committee:
Convener:
Dr. Ram Avtar (Assistant Professor), Graduate School of Environmental Earth Science, Hokkaido University, Japan
Co-convener:
Prof. Teiji Watanabe, Graduate School of Environmental Earth Science, Hokkaido University, Japan
Prof. Olga Tutubalina, Moscow State University (MSU), Russia
Advisory Committee:
Prof. Junichi Yoneda, Prof. Masashi Ohara, Prof. Shiro Tsuyuzaki, Prof. Hideaki Shibata, Prof. Yuichi Hayakawa,
Dr. Ayumu Kotani, HaRP secretariate
Contact and Registration:
Dr. Ram Avtar ram@ees.hokudai.ac.jp ; +81-011-706-2261
If you are interested to participate. please contact to Dr. Ram Avtar for symposium Id and password(ram@ees.hokudai.ac.jp)Registration Fee: Free
Jointly organized by:
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