Earth Observation for improved hydro-geological risk NOWCASTing (EO4NOWCAST)

The EO4NOWCAST experiment, developed by STAM , Artys and Parse Hub was an innovative project aimed at developing an AI-based Decision Support System (DSS) to enhance hydro-geological risk management. This system focused on the nowcasting of severe weather events, such as pluvial floods and landslides. By integrating Earth Observation (EO) data, AI algorithms, and DSS technologies, the project sought to accurately predict short-term natural hazards. The goal was to improve the safety and resilience of urban areas by providing timely, reliable forecasts of hydro-geological conditions. The experiment utilized a combination of soil moisture remote sensing and real-time rain monitoring, employing AI to aid first responders in effectively preventing or mitigating the impacts of floods and landslides. The resulting AI-driven DSS application was designed for widespread adoption across Europe, thereby strengthening member states' preparedness and response to severe weather-related hazards.