An interdisciplinary
AI research and
development platform
for marine
environments
sustainability
Marine observation becomes predictive intelligence for real environmental decisions.
BlueAI is a 48-month coordinated project focused on monitoring, modeling and interpreting marine ecosystems through Artificial Intelligence, combining IoT, advanced sensing, satellite data, Big Data analytics and Digital Twin technologies.
BlueAI focuses on two critical and environmentally relevant settings: Mar Menor and Rías Baixas.
Its core value is moving from reactive observation to predictive, action-oriented intelligence, with early warnings, what-if scenarios, and DSS support for technical, scientific, and management decisions.
To achieve this, BlueAI integrates multisensor acquisition, hybrid AI models and domain knowledge into a shared architecture designed to evolve throughout the full project lifecycle.
BlueAI Intelligence Cycle
Four priorities connect interdisciplinary research, European infrastructure and actionable environmental intelligence.
Build a stable interdisciplinary foundation across AI, oceanography and marine observation.
This objective includes a shared methodology across teams for data capture, validation and scientific exploitation.
Knowledge transfer between AI and marine science profiles is prioritized to accelerate actionable outcomes.
Integrate BlueAI capabilities with EOSC, AIoD, Data Spaces and the DTO ecosystem.
BlueAI aligns with European infrastructures and standards for data and model interoperability.
This supports scale-up, reuse, and effective integration with key strategic initiatives.
Deploy a digital twin and DSS support with what-if scenarios for operational decisions.
The goal is to move from passive monitoring to event anticipation through robust predictive models.
The DSS integrates alerts, scenario simulation and direct support for technical and environmental management decisions.
Improve conservation and management in Mar Menor and Rías Baixas with actionable models.
The end goal is measurable impact on water quality, ecosystem resilience, and response capacity.
Results are expected to become useful tools for research teams, public management and local stakeholders.
A shared technical foundation supports the complete observation-to-action cycle.
First, we establish coordination, infrastructure, and shared rules so the project operates as one coherent system.
Then we run the full cycle from observation to decision-making, turning data into environmental action.
One intelligence architecture is tested across two distinct and environmentally critical marine systems.
Scientific advances are translated into operational, societal and reusable data outcomes.
Expected outcome: stronger AI + marine science integration for ecosystem understanding.
How measured: validated models, reusable methods, and scientific outputs produced throughout the project.
Expected outcome: shift from reactive monitoring to predictive and decision-ready operation.
How measured: DSS uptake in technical workflows and documented use in real decision scenarios.
Expected outcome: better support for public management and stakeholder coordination in marine areas.
How measured: co-created use cases, stakeholder engagement actions, and practical transfer to institutions.
Expected outcome: interoperable data-model architecture ready to scale beyond initial pilots.
How measured: integration readiness, reusable components and extension to new environments.
Impact is delivered progressively across the 48-month roadmap.
Four delivery phases move BlueAI from a shared baseline to transfer and long-term sustainability.
Baseline Ready
Technical setup, requirements and first integrated baseline for the BlueAI stack.
Integration Validated
Infrastructure integration, validation cycle 1, and stakeholder co-creation activities.
Operational Pilot
Second monitoring-modeling-action cycle with model tuning in real decision contexts.
Transfer & Sustainability
DSS/DT consolidation, transfer packages and post-project sustainability pathway.
