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RCN/SAVI: GRAIT-DM
  • People
    • Directors
    • SAVI Founding Members
    • Japanese partners
    • SAVI Members
    • Current and Past Students
  • Publications
  • Datasets and Resources
    • EDNA-Covid Tweets Dataset
    • LITMUS – Landslide Information System
    • Data feeds
    • Educational Materials
  • Events
    • US-Japan Workshop: June 5 – 6 2017
    • NSF & JST Workshop: May 23-24, 2013
  • Related Projects
    • Community driven
    • Technology driven
  • Organizations
  • Contact

SAVI Members

  • Dr. Dimitrios Georgakopoulos (CSIRO, AU)
  • Dr. Naphtali David Rishe (TerraFly, FIU)
  • Vitaly Zaginaev (IWPH, Kyrgyzstan)
  • Dr. Patrick P. Meier

From the Project Proposal

Disasters affect many people in many countries. GRAIT-DM (Global Research on Applying Information Technology for Disaster Management) is an international collaborative effort. The multi-hazard research represented by LITMUS is an international collaboration with Japan, and a growing partnership with many countries affected by landslides such as Brazil, China, India, and Russia. Successful detection and prediction of multi-hazards will have significant human, social, and economic benefits with positive impact on the people around the world.

Recent Posts

  • US-Japan Workshop: June 5 – 6 2017
  • April 2016 Kumamoto Earthquakes
  • Landslide blocks Batote – Kishtwar highway
  • A new publication by GRAIT-DM team has been added
  • Source code for LITMUS is released
  • On a recent landslide in Hiroshima, Japan
  • On a recent mudslide in Washington
  • LITMUS: Landslide Detection by Integrating Multiple Sources
  • List of Landslide Events by USGS
  • The Landslide Blog on AGU Blogosphere

From the Project Proposal

Disasters affect many people in many countries. GRAIT-DM (Global Research on Applying Information Technology for Disaster Management) is an international collaborative effort. The multi-hazard research represented by LITMUS is an international collaboration with Japan, and a growing partnership with many countries affected by landslides such as Brazil, China, India, and Russia. Successful detection and prediction of multi-hazards will have significant human, social, and economic benefits with positive impact on the people around the world.

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