Semi-Automated Mapping of Pockmarks from MBES Data Using Geomorphometry and Machine Learning-Driven Optimization

Abstract

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow based on the CoMMa (Confined Morphologies Mapping) toolbox to classify pockmarks in Flensburg Fjord, Germany–Denmark. Initial detection employed the Bathymetric Position Index (BPI) with intentionally permissive parameters to ensure high recall of morphologically diverse features. Morphometric descriptors were then extracted and used to train a Random Forest classifier, enabling noise reduction and refinement of overinclusive delineations. Validation against expert-derived mappings showed that the model achieved an overall classification accuracy of 86.16%, demonstrating strong performance across the validation area. These findings highlight how integrating a GIS-based geomorphometry toolbox with machine learning yields a reproducible, objective, and scalable approach to seabed mapping, supporting decision-making processes and advancing standardized methodologies in marine geomorphology.

Keywords

• seafloor geomorphology
• geomorphometry
• pockmarks
• seabed mapping
• multibeam echosounder (MBES) data
• CoMMa toolbox