From Spectra to Marine Litter: Multispectral Imaging and AI for Aerial Detection and Monitoring

Project Code: MACKIEWICZ_UEA_ARIES27_CASE

From Spectra to Marine Litter: Multispectral Imaging and AI for Aerial Detection and Monitoring

Project Code: MACKIEWICZ_UEA_ARIES27_CASE

Project Description

Supervisors

Professor Michal Mackiewicz, School of Computing Sciences, University of East Anglia – contact me

Professor Graham Finlayson, UEA

Mr Peter Kohler, Cefas

Dr Julie Bremner, Cefas

 

Scientific Background:

Marine litter threatens ocean health and the coastal livelihoods that depend on it. Scalable automated methods to detect, classify and quantify litter are needed to improve understanding of its sources, pathways and impacts, yet reliable monitoring at scale remains challenging. This PhD will help address that gap, supporting marine litter assessment in the United Kingdom, Europe and beyond, contributing evidence relevant to international action on plastic pollution.

The Centre for Environment, Fisheries and Aquaculture Science (CEFAS) is developing a visible-light deep learning classifier trained on a dataset covering 89 categories of litter, alongside laboratory facilities for characterising material reflectance. This project will extend that work using multispectral and hyperspectral imaging to improve discrimination between material types. A key challenge is to develop robust methods that account for sensor sensitivity, changing daylight illumination and diverse material reflectance properties.

 

Research Methodology

You will combine existing labelled visible-light images with spectral measurements collected using laboratory imaging systems spanning approximately 380 to 1650 nanometres. You will characterise representative litter materials, curate imaging datasets, and develop machine-learning models for material detection and classification.

You will investigate model performance under changing illumination and across different imaging sensors. Approaches will include device-independent representations, transfer learning and domain adaptation. You will evaluate model robustness and explore translation into practical multispectral systems for large-scale drone-based monitoring.

 

Training:

You will be based in the Colour & Imaging Laboratory within the School of Computing Sciences and work closely with scientists and engineers at CEFAS. Training will include imaging principles, laboratory measurement, computer vision, deep learning, geospatial analysis and experimental design, with potential opportunities for fieldwork including drone-flying. You will develop expertise in spectral imaging, artificial intelligence, environmental monitoring, data analysis and interdisciplinary research.

 

Person Specification:

We seek an enthusiastic and curious individual with a scientific, quantitative or computational background and a genuine interest in environmental challenges. Applicants from computer science, mathematics, physics, engineering, environmental science or biological science are warmly encouraged. Prior expertise across all these areas is not expected. Curiosity, analytical thinking and willingness to learn are essential.

Acceptable first degree subject(s): Bachelor’s degree in Computer Science, Physics, Mathematics, Environmental Science, Biological Sciences, Engineering, or another relevant scientific, quantitative or computational discipline.

References

  • Balsi, M., Moroni, M., & Bouchelaghem, S. (2025). Plastic litter detection in the environment using hyperspectral aerial remote sensing and machine learning. Remote Sensing, 17(5), 938. doi: 10.3390/rs17050938.
  • Settembre, G., Gargano, G., & Del Buono, N. (2025). Superpixel-based plastic litter detection in UAV hyperspectral imaging using spectral-textural features. Procedia Computer Science, 270, 4997–5006. doi: 10.1016/j.procs.2025.09.626.
  • Hobley, B., Mackiewicz, M., Bremner, J., Dolphin, T., & Arosio, R. (2023). Crowdsourcing experiment and fully convolutional neural networks for coastal remote sensing of seagrass and macro-algae. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 8734–8746. doi: 10.1109/JSTARS.2023.3312820.
  • Bremner, J.; Petus, C.; Dolphin, T.; Hawes, J.; Beguet, B.; Devlin, M.J. A Seagrass Mapping Toolbox for South Pacific Environments. Remote Sens. 2023, 15, 834. https://doi.org/10.3390/rs15030834
  • Waqas, M., Wong, M. S., Stocchino, A., Abbas, S., Hafeez, S., & Zhu, R. (2023). Marine plastic pollution detection and identification by using remote sensing-meta analysis. Marine Pollution Bulletin, 197, 115746. doi: 10.1016/j.marpolbul.2023.115746.

Key Information

  • This studentship has been shortlisted for funding under the UKRI NERC DLA funding scheme and will commence on 1 October 2027. The closing date for applications is 23:59 on 16 December 2026.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£21,805 p.a. for 2026/27) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.
  • ARIES postgraduate researchers (PGRs) benefit from bespoke training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis. International applicants should check whether there are any conditions of visa or immigration permission that preclude part-time study. All advertised project proposals have been developed with consideration of a safe, inclusive and appropriate research and fieldwork environment with respect to protected characteristics. If you have any concerns, please contact us.
  • For further information, please contact the supervisor. To apply for this Studentship, follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential and will be stored on the UEA SharePoint server. Data will not be shared with those involved in making decisions on the award of Studentships and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be asked to complete the form by the University to which you apply.
  • ARIES studentships are subject to UKRI terms and conditions. Postgraduate Researchers are expected to live within reasonable distance of their host organisation for the duration of their studentship. Please see https://www.ukri.org/publications/terms-and-conditions-for-training-funding/ for more information.

Apply Now

Apply now via the University of East Anglia Application Portal