The Role of Artificial Intelligence in Automating Bathymetric Data Analysis and Feature Detection

Bathymetric mapping is essential for safe navigation, coastal infrastructure development, ecosystem monitoring, and understanding marine geological processes. As survey sensors become increasingly capable, the volume and resolution of bathymetric data have grown exponentially, exceeding the capacity of traditional manual analysis techniques. Conventional workflows, although proven, remain resource-intensive, subjective, and prone to inter-operator variability. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, offers transformative potential to automate feature detection, classification, and anomaly identification in bathymetric datasets. This research investigates the emerging role of AI in automating bathymetric data analysis, reviewing state-of-the- art techniques such as convolutional neural networks, transfer learning, and unsupervised clustering. The paper highlights case studies demonstrating successful applications in shipwreck detection, habitat classification, and geomorphological segmentation, while also discussing critical challenges around data scarcity, generalization across diverse seafloor types, and the interpretability of AI models. Overall, the synthesis of current knowledge suggests that AI-supported bathymetric workflows can substantially improve operational efficiency, consistency, and accuracy for marine geospatial applications, while underscoring the need for further research in explainable and transferable frameworks.