Start Date: May 16, 2024
Vasileios Vatellis
In the context of this doctoral dissertation, we aim to develop advanced deep learning techniques and multimodal approaches for the detection and prediction of extreme weather events, as well as the assessment of their short- and long-term impacts on agriculture. These methods will integrate and analyse diverse data sources, including satellite imagery, meteorological records, and climate reanalysis datasets. By leveraging the rich and heterogeneous information provided by Earth observation systems—such as the Sentinel satellite missions and climate datasets like ERA5—our goal is to improve the accuracy and reliability of predictive models. This research seeks to support early warning systems, enhance agricultural resilience, and contribute to more informed decision-making in the face of climate-related risks.