UNU-INWEH is developing a flood mapping and future flood risk prediction tool. This tool consists of two modules; a flood mapping module that addresses the data gap of historical flood maps and a flood risk predicting module, which addresses the issue of possible risk in the future. The  historical  flood  mapping  module  uses  a  water  classification  algorithm  (Modified  Normalized Difference Water Index) applied to ‘stacks’ of historical Landsat and Sentinel 2 satellite imagery to reveal patterns of inundationover space and time across the landscapes. The prototype of the tool is ready, which uses Landsat data to identify water patterns.The second module will use AI models to predict the future flood risk for a given area. The AI models will  be  trained  usingthe  historical  flood  maps  from  the  first  module,  and  open  temporal  datasets including land use land cover, population, infrastructure, precipitation, temperature, and sex and age disaggregated  socio-economic  data.  This  module  will  help  identify  the  most  flood-risk  areas  for  the future. We are looking for an intern who can work remotely to assist in the development of the first module of the tool by integrating the Harmonized Landsat Sentinel-2 data.
Description of Responsibilities

• Integrate the Harmonized Landsat Sentinel–2 data.
• Test the integration against recent flood events (validation data is available).
• Optimize the HFMT system flow where possible.
• Undertake additional tasks as assigned by the supervisor