Slow deep-seated mass movements are widespread players of slope dynamics, with different mechanisms depending on involved materials, geomorphic settings and external forcing. Alpine settings, characterized by crystalline rocks and para/periglacial environments, are extensively affected by slow rock-slope deformations, deep-seated… Read more rockslides and periglacial features (e.g. rock glaciers). Mountains with fluvial-dominated topography in sedimentary rocks (i.e. Apennines) are typically affected by long-lived earthflows. These processes have different controls, show different spatio-temporal deformation patterns and rates, and threaten lives, activities, and infrastructures in different ways. Mitigating risks posed by deep-seated mass movements requires capabilities to: a) detect and classify different processes systematically and rapidly over wide areas; b) monitor their evolution towards possible destabilisation; c) predict interactions with elements at risk. Current regional scale analyses rely on geomorphological techniques supported by remote sensing to capture processes and their spatio-temporal evolution. These approaches are accurate but time consuming and difficult to update. Such gaps could be filled using artificial intelligence techniques, yet their applications to mass movements are still few, mostly limited to interpretation of optical images and missing robust process-oriented approaches. The MIRAGE project will provide methods and tools for the semi-automated detection, classification and kinematic characterization of deep-seated mass movements through the combination of multi-scale geomorphological data, spaceborne InSAR and deep learning techniques. The construction of libraries of expert-interpreted InSAR phase signal features, corresponding to different mass movements, will allow training and validating a Convolutional Neural Network (CNN).This will be tested with routinely generated SAR interferograms, to produce automated multi-temporal maps able to detect and classify mass movements, evaluate their displacement rates and temporal evolution. These results will provide policy making and engineering communities with new tools to quantify hazards related to specific processes, anticipate their critical evolution and support the prioritisation, planning and design of risk mitigation measures. MIRAGE addresses strategic research topics of the Italian “Piano Nazionale di Ripresa e Resilienza''(PNRR; Mission 4, Component 2, investments 1.3-1.4) as well as typical risk management missions of Civil Protection and regional authorities. Project goals will be achieved by exploiting the complementary expertise of three research units (UNIMIB, UNIBO, CNR), including the applied geomorphological and engineering characterization, monitoring and modelling of mass movements, InSAR processing and artificial intelligence. Activities will be deployed in six interrelated work packages and disseminated to scientific and technical communities.