Abstract

SeaWatchAI revolutionizes ocean data management by offering a proactive approach to environmental monitoring and data management for experts and non-experts at scale. By integrating advanced AI capabilities provided by the Bluemvmt platform with a user-friendly front-end, SeaWatchAI addresses the pressing challenges in hydrography, acoustic data processing, and Uncrewed Surface Vehicle (USV) operations. The platform focuses on three main areas: Collecting, processing, and validating data; synthesizing different types of data from disparate sources, leading to new understandings of the oceanographic environment; and data discovery, accessing, manipulation, storage, and dissemination. By leveraging hybrid generative AI, SeaWatchAI assists with edge processing, prediction, and uncrewed vessel data acquisitions/operations, providing a scalable and adaptable solution for various maritime activities. This alignment with strategic goals helps develop and deploy innovative, cost-effective solutions for managing data collection and addressing environmental challenges.

We present a case study to explore the application of generative AI in enhancing the quality of acoustic Bottom Scattering Loss (BSL) data by allowing the AI to select the best approach to filling data gaps due to random drop-outs, and correcting errors based on outlier analysis. Traditional user-driven methods for BSL QA/QC are time-consuming, require significant human and material resources, and often suffer from data fragmentation. By leveraging generative AI-assisted data correction and imputation, error corrections using Isolation Forest analysis and gap filling using multiple imputation methods are demonstrated. The AI-corrected data shows a closer alignment with the true scattering loss, reducing the mean squared error and improving overall data integrity.

Keywords:Hybrid generative; Ocean data; AI; Uncrewed surface; Maritime activities; Isolation forest analysis; SeaWatchAI