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Can artificial intelligence (AI) help protect 麻豆无码版鈥檚 coastal waters from marine oil spills? Mohamed Elsheref, Ph.D., a postdoctoral researcher in UAA鈥檚 Department of Chemistry, is the lead author on a scientific paper published last month in the Journal of Environmental Chemical Engineering. In the article, titled Elsheref and his co-authors presented a deep learning framework, OilSpillNet, which offers possibilities for automated detection of marine oil spills. Rapid and accurate spill detection is critical for protecting marine ecosystems, fisheries and coastal communities, where early response can significantly reduce environmental and economic damage. 

The traditional method for monitoring marine oil spills involves humans manually reviewing images from synthetic aperture radar (SAR). Elsheref noted that this process can be 鈥渟low, labor-intensive and error-prone鈥� as natural phenomena like algal blooms or low-wind areas can 鈥渕imic oil signatures鈥� and create false positives. While trained specialists can discern the difference, this manual review 鈥渃annot scale to the vast data volumes generated by frequent satellite overpasses, nor do they enable the rapid alerts necessary for emergency response,鈥� Elsheref wrote. 

鈥淏y integrating environmental science expertise with artificial intelligence and computer vision methods, OilSpillNet enables automated, large-scale monitoring of satellite imagery鈥攕upporting faster, data-driven decision-making for coastal management and spill response,鈥� said Elsheref.

After training OilSpillNet on a 鈥渃urated [National Oceanic and Atmospheric Administration] dataset of 206 SAR images,鈥� Elsheref and his co-authors found that the framework was more accurate than other models currently available and was better able to distinguish between 鈥渙il and background under challenging conditions.鈥� 

The project was developed in collaboration with researchers at the University of New Orleans (UNO) in a multidisciplinary team led by UNO鈥檚 Professor Md Tamjidul Hoque, integrating expertise in computer science and environmental analytical chemistry. The model鈥檚 code and a working software version of OilSpillNet are .

In 2025, prior to coming to UAA, Elsheref completed a Ph.D. in environmental analytical chemistry and a master鈥檚 degree in computer science in parallel at the University of New Orleans. Elsheref now works in UAA鈥檚 Analytical Science for Environmental Toxicology (ASET) Lab with his advisor, Patrick Tomco, Ph.D., associate professor. 

Elsheref and Tomco are currently working on a project funded through , titled 鈥淗ydrocarbon Oxidation Products of the Unresolved Complex Mixture (UCM): Advancing New Approaches in Chemical Characterization and Biological Effects.鈥�