Automated seafloor feature recognition in Side-Scan Sonar data using Segment Anything Models (SAM): a case study from Apulian coastal nearshores

Human activities are increasingly degrading European coastal seabed, highlighting the need for efficient monitoring tools. To address these impacts, the Marine Strategy Framework Directive (MSFD) was established in 2008 with the aim of protecting and preserving marine ecosystem while ensuring their sustainable use.

Within this framework, high-resolution seafloor mapping represents a fundamental tool for coastal governance, habitat monitoring and marine geological studies. Fine-scale survey using Side-Scan Sonar (SSS), often integrated with AUV systems, provides detailed information on seabed morphology and acoustic facies, supporting habitat mapping and coastal management.

Recent advances in artificial intelligence (AI) and machine learning (ML), combined with Geographic Information Systems (GIS), have significantly improved the automated interpretation and mapping of submerged morphological features from marine geophysical data. In this context, this study investigates the application of computer vision techniques to nearshore environments along the Italian coastline, with a specific focus on the Segment Anything Model (SAM) framework.

We evaluate both the foundation-model implementation of SAM (SAM2) and its prompt-driven variant (SAM3) for the detection and classification of seafloor features in high-resolution SSS mosaics. The analysis was conducted on three nearshore areas in the Apulia region (southern Italy): Torre Guaceto (BR), Leporano (TA) and Zapponeta (FG). Using SAM2, we implemented a supervised workflow in which manually segmented features, including ripple marks, coralligenous formations, seagrass beds, sandy plains and rocky outcrops were used to train a Contrastive Captioner (CoCa) classifier via transfer learning. This approach achieved a test Macro F1 score of approximately 0.90.

In parallel, the text-promptable SAM3 model was employed for zero-shot segmentation of small, high-backscatter point-like features (“white dots”). These features were validated through an independent AUV-based video survey and confirmed to correspond to coralligenous formations. This result demonstrates the capability of SAM3 to rapidly extract geologically meaningful targets without requiring prior training.

Future developments will focus on alternative strategies and model refinement to further improve detection accuracy and robustness.