Machine learning–based reconstruction of Late-Quaternary coastal paleo-landscapes: an AI framework

Artificial Intelligence is increasingly reshaping geomorphological research by enabling scalable, data-driven analyses of complex Earth surface processes. In this contribution, we present a supervised machine-learning framework for reconstructing Late-Quaternary coastal paleo-landscapes, applied to the rocky coasts of the Cilento Promontory (southern Tyrrhenian Sea), a tectonically quasi-stable sector preserving well-constrained sea-level indicators.
We trained a Random Forest classifier on an expert-labelled geomorphological dataset integrating DEM-derived morphometric parameters, lithology, distance from the coastline, and field-validated paleo-environmental markers. The model was developed within a fully reproducible workflow and validated against independent geomorphological mapping and sea-level proxy datasets.
Results demonstrate high classification performance and the ability to automatically discriminate between Last Interglacial paleo-sea cliffs and polycyclic, currently active coastal cliffs across different lithological contexts. The AI-based approach overcomes key limitations of traditional “bathtub” methods, allowing the detection of relict and partially buried landforms and extending paleo-landscape reconstructions into areas lacking direct field evidence.
Beyond the specific case study, this work illustrates how machine-learning approaches can be effectively integrated with geomorphological knowledge to reconstruct complex coastal paleo-landscapes. The proposed framework allows the identification of inherited and partially obscured landforms that are difficult to detect through traditional methods alone, offering a transferable tool for investigating long-term coastal evolution. This integration of AI and geomorphology provides new insights into the geomorphic response of rocky coasts to Quaternary sea-level fluctuations and climatic forcing.