A Semi-Automatic-Based Approach to the Extraction of Underwater Archaeological Features from Ultra-High-Resolution Bathymetric Data: The Case of the Submerged Baia Archaeological Park

Abstract

Coastal and underwater archaeological sites pose significant challenges in terms of investigation, conservation, valorisation, and management. These sites are often at risk due to climate change and various human-made impacts such as urban expansion, maritime pollution, and natural deterioration. However, advances in remote sensing (RS) and Earth observation (EO) technologies applied to cultural heritage (CH) sites have led to the development of various techniques for underwater cultural heritage (UCH) exploration. The aim of this work was the evaluation of an integrated methodological approach using ultra-high-resolution (UHR) bathymetric data to aid in the identification and interpretation of submerged archaeological contexts. The study focused on a selected area of the submerged Archaeological Park of Baia (Campi Flegrei, south Italy) as a test site. The study highlighted the potential of an approach based on UHR digital bathymetric model (DBM) derivatives and the use of machine learning and statistical techniques to automatically extract and discriminate features of archaeological interest from other components of the seabed substrate. The results achieved accuracy rates of around 90% and created a georeferenced vector map similar to that usually drawn by hand by archaeologists.

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