DK-Future: Probabilistic Geospatial Machine Learning for Predicting Future Danish Land Use under Compound Climate Impact

DK-Future will develop novel geographical machine learning models, termed probabilistic GeoML, designed to predict future land use changes by integrating probabilistic approaches that incorporate geographical data and accounting for uncertainties in climate change scenarios. These models will leverage historical Earth observation and climate projection data to forecast land use changes in Denmark under compound climate impacts. With its low-lying terrain and long coastline, Denmark is highly vulnerable to climate-induced land use changes, highlighting the need for forecasting tools to support proactive land use management with the associated uncertainties. This effort requires advances in probabilistic modeling of complex spatio-temporal processes, in close synergy with the geographical sciences, incorporating concepts like spatial proximity and autocorrelation into ML models. This blend of probabilistic ML (PI1) and land use and climate change dynamics (PI2) will open new research avenues for modeling spatio-temporal processes and expand our understanding of how climate change impacts land use, under uncertain climate projections. This research is essential for decision-makers, providing insights into extreme events and their likelihood.