Integrating Unmanned Platforms and Deep Learning Technologies for Enhanced Ocean Observation and Risk Mitigation in Ocean Engineering

Ocean observation today is confronted with significant challenges, including limited adaptability to complex environments, low efficiency in handling multi-source data, and high operational and maintenance costs. While unmanned surface and underwater vehicles (USVs/UUVs) can gather high-resolution data—capturing biological behavior, tracking equipment, and measuring environmental parameters—their long-term deployment is often hindered by biofouling and mechanical failures. Moreover, traditional data analyses, heavily reliant on manual interpretation, struggle to provide real-time decision support. Advances in deep learning offer promising solutions for multimodal data integration and risk prediction. Meanwhile, biomimicry—through antifouling microstructures and efficient propulsion mechanisms—can enhance equipment reliability. This interdisciplinary study aims to develop an intelligent, adaptive ocean observation system to overcome technical barriers in marine environmental monitoring, risk assessment, and ecological conservation.



This Research Topic seeks to address several key issues in ocean observation and engineering. We aim to:

1. Enhance the collaborative optimization of unmanned systems by developing new algorithms for multi-machine coordination, improving data collection on biological behaviors and equipment status amidst complex sea conditions.

2. Establish an intelligent data analysis framework using advanced spatiotemporal deep learning models, such as Graph Neural Networks (GNN) and Transformers, for automating biological distribution predictions, anomaly detection in trajectories, and antifouling failure warnings.

3. Develop a dynamic risk assessment model that integrates data-driven and physical modeling approaches, quantifying site selection and coupling risks for marine engineering equipment to build a resilient observation and maritime transportation system.

4. Innovate bionic technologies inspired by the sensory mechanisms and movement patterns of marine life, creating cost-effective materials and algorithms to enhance the biomimetic intelligence of marine technologies.

5. Create a comprehensive and open-source data management platform supporting the entire observation-analysis-decision-making chain, fostering the sustainable development of marine resources and ecological stewardship.



This Research Topic welcomes contributions addressing the following themes and invites a variety of manuscript types, including original research, reviews, and methodological papers:

• Autonomous navigation and collaborative control of USV/UUV/AUV systems

• Task planning and resource optimization for unmanned systems

• Integration of multimodal observation sensors

• Advanced data analytics and modeling for ocean observations

• Identification and prediction of marine organism behaviors and population dynamics

• Multi-source data fusion and spatiotemporal correlation analysis of observation equipment

• Sustainable development through marine observation networks

• Ecological response modeling informed by marine environmental parameters

• Innovations in bionic propulsion and energy management

• Applications of biomimetic technology in marine engineering

• Strategies for unmanned systems inspired by marine biological group behaviors

• Enhancements in marine data completion and scenario simulation

• Explainability (XAI) and uncertainty quantification in marine AI systems

• Eco-friendly designs mitigating the impact of unmanned systems on marine life

• Projects focusing on marine ranches and ecological restoration

• Risk assessment and strategic siting of marine engineering equipment