Techniques and methods for seafloor topography mapping: past, present, and future

Abstract Detailed mapping of seafloor topography is essential for understanding seafloor evolution, ensuring navigational safety, and discovering mineral resources. As global environmental conditions continue to deteriorate, various international and regional initiatives have been launched to accelerate seafloor topography mapping, yielding valuable data. Currently, only about a quarter of the seafloor has been directly mapped, observed, and explored due to limitations in traditional detection techniques. However, artificial intelligence, particularly machine learning, is progressively overcoming these constraints with its advanced data processing and analysis capabilities. In recent years, machine learning has increasingly emerged as an alternative to traditional methods, particularly for mapping both open-ocean and shallow-sea topography. This paper first introduces traditional seafloor topography detection techniques and the global topography models developed using them. It then examines the application of machine learning in seafloor mapping before concluding with the challenges and future prospects of intelligent seafloor mapping, along with relevant recommendations.