Synthetic Sonar Generation: Leveraging Algorithmic and AI-Based Approaches for Enhanced Underwater Mapping and Exploration

The acquisition of high-quality sonar bathymetry and sidescan imagery is crucial for various underwater applications, including seafloor mapping, habitat characterization, and object detection. However, the collection of such data can be time-consuming, costly, and often limited by environmental and logistical constraints. While synthetic data cannot replace the value of real-world data, it can be a valuable supplement. Specifically, synthetic sonar bathymetry and sidescan imagery can be used to affordably train AI models, facilitate education and training, and enhance the analysis and validation of real data. With these benefits in mind, we propose a novel framework for synthetic generation of sonar bathymetry and sidescan imagery using algorithmic and AI-based tools, which can help augment and support the use of real data in underwater applications.