Maritime transportation is the backbone of global trade, carrying over 80% of international goods while operating in increasingly crowded, complex, and sensitive ocean and coastal environments. As vessel traffic intensifies and routes expand into ecologically fragile regions, ensuring safe, secure, and low-impact maritime operations has become a critical challenge. The rapid growth of big data—spanning automatic identification systems (AIS), satellite remote sensing, radar, onboard sensors, and port and logistics information—has created new opportunities to enhance situational awareness, prevent collision-related spills, reduce inefficient routing and associated emissions, and support resilient emergency response. Yet, the integration of these heterogeneous data streams into operational safety, environmental protection, and policy frameworks remains fragmented. This Research Topic aims to explore how data-driven methods can underpin safer, nature-positive, and climate-aware maritime transportation systems.
The maritime domain faces mounting safety, security, and environmental risks, from growing vessel traffic and complex trade networks to emerging threats like cyberattacks, illegal activities, and climate-driven extreme events. Traditional safety management, often reliant on sparse or static data, limits real-time situational awareness and proactive mitigation. However, large volumes of multi-source data—including AIS, radar, satellite imagery, and shipboard sensor streams—now offer unprecedented potential for forecasting hazards, optimizing routes, and minimizing emissions and spill likelihood.
This Research Topic aims to bridge data science, ocean and coastal system understanding, and maritime governance to develop actionable, nature-positive safety and security solutions. We invite contributions that advance big data analytics, AI/machine learning, and decision-support systems for real-time risk prediction, anomaly/spill precursor detection, resilient routing, traffic management, and environmentally aware emergency response. We particularly encourage studies quantifying measurable outcomes—such as reduced collision probability, lower emergency-related emissions, or improved spill prevention—and linking these benefits to socio-ecological resilience, blue natural capital, and sustainable maritime transport pathways.
This Research Topic invites original research, reviews, and methodological contributions demonstrating how big data and AI can strengthen maritime safety and security, fostering nature-positive, low-impact, and resilient ocean operations. We particularly encourage interdisciplinary work linking data science with ocean/coastal science and governance, emphasizing quantifiable gains in safety, environmental performance, and socio-ecological outcomes.
Indicative themes include, but are not limited to:
• Multi-source data fusion and maritime digital twins for whole-system awareness and resilience assessment.
• Advanced risk prediction and hazard analytics to reduce collision, grounding, ship-strike probabilities, and spill consequences.
• Real-time anomaly and unsafe-behavior detection protecting security and sensitive marine ecosystems.
• Emissions- and noise-aware routing and traffic management for safer, cleaner operations.
• Environmentally optimized emergency maritime response minimizing secondary damage and response-phase emissions.
• Maritime cybersecurity solutions safeguarding navigation, sensing, or environmental-compliance systems.
• Governance, policy, and data-sharing frameworks supporting blue natural capital and coastal community resilience.

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Data-Driven Maritime Safety and Security for Sustainable Oceans: Towards Nature-Positive, Low-Impact Operations