Frequent disasters have been a serious threat to human life and social development.   Disaster prevention, disaster reduction and disaster relief are eternal subjects for human survival and development. It is imperative to build the global disaster database, and the reasonable and effective metadata standard or best practice is the premise for disaster database construction. This workshop will invite domestic and international experts in disaster risk reduction related fields, to discuss the issues about but not limited to   metadata standard and best practice, data sharing, Artificial Intelligence (AI) and Big data application, regional and national experiments for Disaster Risk Reduction (DRR).

The topics of the presentations include but are not limited to:

(1) Disaster data sharing

(2) Knowledge service application of DRR, including climate risks in travels

(3) Knowledge service platform of DRR

(4) Application of AI and Big data in DRR

As a section of the International Conference on Silk Roads Risk Reduction and Sustainable Development (ICSRRSD 2019), the session 22 “Disaster Risk Reduction Knowledge Service” will be held at Beijing International Convention Center, China on the morning of May 12, 2019.

Organizer:

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR, CAS) ,

International Knowledge Centre for Engineering Sciences and Technology under the Auspices of UNESCO (IKCEST).

Chair:

Prof. Juanle Wang, vice director of Department for Geo-data Science and Sharing,   IGSNRR, CAS, and executive director of the Disaster Risk Reduction Knowledge   Service of IKCEST,

Prof. Elena Grigorieva, Leading Researcher, Laboratory of regional social-economic   systems, Institute for Complex Analysis of Regional Problems, Far-Eastern   Branch, Russian Academy of Sciences (ICARP FEB RAS)

Date: 12 May, 2019

Venue: Beijing International Convention Center, China

Contact: Ms. Yuelei Yuan: yuanyl@lreis.ac.cn

Website: http://www.sidrr.com/portal/article/index/id/77.html