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Reconstructing Late Quaternary coastal landscapes by a machine-learning framework

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Abstract

Coastal landforms, particularly sea cliffs and associated wave-cut platforms, 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-sea cliffs and polycyclic sea cliffs – along the south-Tyrrhenian. Using DTM and morphometric indicators, our model, based on a RandomForestClassifier trained on expert-based cartography and independently validated, accurately identifies the spatial signatures of Quaternary coastal evolution. These results are cross validated against independent geomorphological mapping and sea-level reconstruction datasets. The integration of geomorphological classification with 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, providing insight into the imprint of climatic forcing on their morphology. This approach offers a scalable framework for investigating past climate–landscape interactions and for supporting future coastal hazard assessments under changing climate conditions.

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Data availability

The data that support the findings of this study are contained in the manuscript and supplementary materials.

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Acknowledgements

This work was developed within the scientific framework of the following projects, benefiting from discussions held during the meetings: •INQUA INR ONSEA – Evolution of Seascapes (INQUA CMP Project 2404); •context-AwaRe deCision-making for Autonomus unmmaneD vehicles in mArine environmental monitoring (ARCAD-IA) project (PE00000013_1 – CUP E63C22002150007) cascade call of the Future Artificial Intelligence Research (FAIR) project Spoke 3 – Resilient AI, within the National Recovery and Resilience Plan (PNRR) of the Italian Ministry of University and Research (MUR); •Working Group of the International Association of Geomorphology in Coastal Geoarchaeology •European Union – Next-GenerationEU – National Recovery and Resilience Plan (NRRP) – MISSION 4 COMPONENT 2, INVESTIMENT N. 1.1, CALL PRIN 2022 D.D. 104 02- 02-2022 – PRIN_2022ZSMRXJ “GAIA project- Geomorphological and hydrogeological vulnerability of Italian coastal areas in response to sea level rise and marine extreme events CUP I53D23002130006. We are grateful to the Soprintendenza Archeologia, Belle Arti e Paesaggio for the provinces of Salerno and Avellino for their collaboration and for authorizing fieldwork activities in the study area. We sincerely thank Luigi De Luca for his valuable support in drone data acquisition and processing, and Francesco Peluso for his indispensable support and insightful discussions throughout the research. This study made use of the following softwares: ArcMap 10.4 (Esri, https://www.esri.com), QGIS version 3.38.3 (QGIS Development Team, https://qgis.org), and Inkscape version 1.4.2 (Inkscape Project, https://inkscape.org). Digital Terrain Model (DTM) data were obtained from TINITALY DEM (https://tinitaly.pi.ingv.it/), DBM from EMODnet (https://emodnet.ec.europa.eu/). Original geological data were based on OneGeology (https://portal.onegeology.org/OnegeologyGlobal/) and the geological sketch by Monti et al. (2014): Carta Geologica d’Italia Sapri-Foglio 520 della carta 1:50.000 dell’IGM. All figures and illustrations were created by the authors. We sincerely thank the Editor and the Reviewers for their constructive comments and insightful suggestions, which significantly improved the clarity and quality of this manuscript.

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  1. Department of Science and Technology, University of Naples Parthenope, Naples, Italy

    Gaia Mattei, Alessia Sorrentino, Gerardo Pappone, Angelo Ciaramella & Pietro P. C. Aucelli

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  1. Gaia Mattei
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  2. Alessia Sorrentino
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  3. Gerardo Pappone
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  4. Angelo Ciaramella
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  5. Pietro P. C. Aucelli
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Contributions

GM contributed to the conception, acquisition, analysis, interpretation of data, creation of new software used in the work, and writing of the draft and revised versions. AS contributed to the conception, acquisition, analysis, interpretation of data, creation of new software used in the work, and writing of the draft and revised versions. GP contributed to the conception and supervision. AC contributed to the conception and creation of new software used in the work. PPCA contributed to the conception, analysis, interpretation of data, and supervision. All authors reviewed the manuscript.

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Alessia Sorrentino.

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Mattei, G., Sorrentino, A., Pappone, G. et al. Reconstructing Late Quaternary coastal landscapes by a machine-learning framework.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31006-0

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  • DOI: https://doi.org/10.1038/s41598-025-31006-0

Keywords

  • Coastal landforms
  • Coastal change
  • Sea level change
  • Coastal paleo-landscape
  • Automated mapping
  • Mediterranean area

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