Vietnam has witnessed outstanding economic growth hindered by various natural hazards, including floods, drought, landslides, coastal erosion, etc. According to the United Nations Office for Disaster Risk Reduction (UNDRR), the country has suffered an average annual loss due to natural disasters of 1-1.5 % of the GDP over the past three decades. With the aim of achieving sustainable development across the country, targeted efforts are required to reinforce disaster and climate risk reduction research and policymaking. However, the lack of specialists in the area of climate change affected the operation process. Therefore, implementing Digital geospatial twins and Earth Observation project (CADEO) will strengthen the human resource training for both the existing and future qualified labor force. The CADEO project aims at designing, implementing, and teaching innovative courses connected to climate change adaptation and its enabling technologies within different and interdisciplinary HEIs postgraduate study programs (master’s level) across Vietnam. In particular, the CADEO project will implement 4 courses for higher education programs focusing on:
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Earth Observation (10 ECTs): The course will provide in-depth knowledge on theories, techniques, and tools for the exploitation of data and services based on satellite and in situ acquired geospatial observations.
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Digital Twin Earth (5 ECTs): The course will provide an understanding of both theoretical frameworks and digital ecosystems required for the implementation of the Digital Twin(s) of the Earth and their added value in the next generation monitoring and forecasting operations of natural and human activities.
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Geospatial Web Applications (5 ECTs): The course will provide programming skills and abilities to manage, share and present geospatial data using backend and frontend Web technologies.
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Geospatial Intelligence (10 ECTs): The course will provide in-depth knowledge of both theoretical and computer-assisted statistical techniques, including machine learning, devoted to geospatial data analysis.
These courses will be developed in “distance” and “blended” modes, creating more opportunities for course participants to carry out parts of the coursework from another location than the university. Moreover, the deployment of a modern and shared eLearning system will support the envisaged teaching approach and allow the material to be easily empowered, improved, and maintained both during and beyond the project’s lifetime.