Ongoing projects

Deepfaking ecosystem response to climate extremes

Accurately predicting how ecosystems will respond to future climate extremes has been a challenge due to the limitations of current vegetation models and the lack of precise data to constrain them. To overcome these obstacles, the DeepV project proposes the development of data-driven, spatially explicit ecosystem response models using remote sensing data and conditional Generative Adversarial Networks (cGANs). By harnessing the power of cGANs, DeepV aims to generate realistic satellite image time series of ecosystem response based on environmental conditions and hydro-meteorological data, enabling climate-to-ecosystem response rule establishment and ecosystem sensitivity assessment.