Abstract Coastal landforms preserve key evidence of past sea-level fluctuations, tectonic activity, and paleoclimate variability. In this study, we implement a supervised machine learning approach, trained on an original, expert-labeled geomorphological dataset, to detect and classify inherited and active coastal features - such as paleo-seacliffs and polycyclic sea cliffs - along the south-Tyrrhenian. Using high-resolution DTM and morphometric indicators, our model accurately identifies the spatial signatures of Quaternary coastal evolution. These results are cross-validated against independent geological mapping, and sea-level reconstruction datasets. The integration of geomorphological classification with paleo–sea level markers enables us to reconstruct coastal morphogenesis in relation to the last interglacial cycle. Our findings highlight the potential of machine learning to automate the identification of coastal paleo-landscapes and contribute to refining the timing and extent of marine transgressions and regressions across the Mediterranean. This approach offers a scalable framework for investigating past climate–landscape interactions and for supporting future coastal hazard assessments under changing climate conditions.
Mattei et al. (Mon,) studied this question.