Researchers at Sorbonne University Abu Dhabi (SUAD) have developed an artificial intelligence system that can automatically detect and identify fish species in underwater video footage from the Southern Arabian Gulf, helping address a major bottleneck in marine biodiversity monitoring.
By reducing the time needed to manually analyse hours of footage, the system can support more scalable biodiversity monitoring, evidence-based marine management and the UAE’s commitment to UN Sustainable Development Goal 14: Life Below Water.
The project, developed by the Sorbonne Center for Artificial Intelligence (SCAI) at Sorbonne University Abu Dhabi, in collaboration with Emirates Nature–WWF and the American University of Sharjah (AUS), brings together expertise in artificial intelligence, marine conservation, and regional fish ecology. SUAD leads the AI and computer-vision development; Emirates Nature–WWF contributes marine conservation expertise, field-based biodiversity monitoring and the application of the research to conservation and management needs, and AUS contributes expertise in fish ecology, taxonomy and Arabian Gulf environmental conditions.
The research was recently presented at OCEANS 2026 Monterey in California by Sorbonne University Abu Dhabi postdoctoral researcher Dr. Andrea Mecchina. The initial scientific study focused on five fish species selected for their ecological and fisheries relevance, with the model reliably detecting and correctly identifying the large majority of fish on an independent test dataset it had not seen during training. The project has also progressed to an initial validated dataset covering ten species, with the next phase focused on expanding training and validation across a much larger regional archive of underwater footage, additional species and a wider range of environmental conditions.
The results represent an initial proof of concept, with further testing planned across additional species, locations, seasons and environmental conditions before the approach can be applied at broader monitoring scales.
Dr. Giulia De Masi, Associate Professor at Sorbonne University Abu Dhabi and lead researcher on the project, said: “Turning large volumes of underwater imagery into useful ecological information is a complex challenge, and this project demonstrates the strength of the interdisciplinary research being advanced at Sorbonne University Abu Dhabi. By bringing together expertise in artificial intelligence, computer vision and marine science, we are developing tools that can support long-term monitoring, deepen our understanding of changes in fish communities and strengthen the evidence available for conservation and marine management across the Arabian Gulf.”
Dr. Daniel Mateos-Molina, Head of Marine Conservation and Biodiversity at Emirates Nature-WWF, who conceived the project, said: “Effective conservation depends on our ability to understand how marine ecosystems are changing over time. By combining field-based ecological monitoring with artificial intelligence, we have an opportunity to turn those observations into usable evidence much faster and at a much larger scale. Equally important is bringing together conservation practitioners, universities and experts in the UAE to develop solutions that respond to real conservation needs. This collaboration shows how applied research can translate scientific and technological expertise into practical tools that strengthen our ability to monitor biodiversity and ultimately support better conservation and management decisions for the UAE’s marine ecosystems.”
Dr. Ivonne Bejarano, Associate Professor at the American University of Sharjah, said: “Despite the ecological and economic importance of fish communities in the Arabian Gulf, important gaps remain in our understanding of their diversity, distribution and changes over time. By combining our knowledge of Arabian Gulf fish biodiversity with AI and extensive underwater imagery, we are developing an approach that can make long-term monitoring of fish communities more efficient and scalable, while maintaining the scientific rigor needed to support conservation and management.”
The Southern Arabian Gulf is a shallow, highly saline marine environment where summer sea temperatures frequently exceed 33°C and can locally approach 38°C. Its marine ecosystems also face pressures from coastal development, shipping, desalination and other human activity. Understanding which species are present, how populations change over time, and how fish communities respond to these pressures is important for effective conservation and marine management.
One of the tools scientists use to collect this information is the Baited Remote Underwater Video System (BRUVS): an underwater camera placed on the seabed with bait to attract fish into view. The method is non-extractive and repeatable, allowing researchers to survey fish communities across many locations without capturing animals.
Using underwater imagery collected across UAE coastal waters and offshore islands, the SUAD-led team developed a computer-vision pipeline designed to recognise fish under difficult underwater conditions such as turbidity, variable illumination, partial obstruction and multiple animals appearing in the same frame.
Five species were included in the OCEANS 2026 study: the orange-spotted grouper, orange-spotted jack, pink ear emperor, cobia and Spanish mackerel. Future development includes combining detection with tracking and abundance measures and exploring lighter versions that could eventually operate onboard underwater robotic systems for near-real-time processing. At the Jubail Mangrove Innovation Centre, the technology is also planned to be showcased as an example of AI-enabled marine monitoring.
Supported through the University’s SAFIR-funded research programme, the project forms part of SUAD’s wider research in artificial intelligence and marine science.
For more information, please visit: https://www.sorbonne.ae/
