From Spectra to Marine Litter: Multispectral Imaging and AI for Aerial Detection and Monitoring (MACKIEWICZ_ARIES27_CASE)
Key details
- Application Deadline
- 16 December 2026 23:59 UK Time
- Location
- UEA
- Funding Type
- Competition funded project (Students worldwide)
- Start Date
- 1 October 2027
- Mode of Study
- Full-time or Part-time
- Programme Type
- PhD
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Welcome to Norwich
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Project Description
Primary Supervisor - Professor Michal Mackiewicz
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.
Entry Requirements
At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).
Funding
ARIES studentships are subject to UKRI terms and conditions. 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 is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Projects have been developed with consideration of a safe, inclusive and appropriate research and fieldwork environment. Academic qualifications are considered alongside non-academic experience, with equal weighting given to experience and potential.
After submitting their application for the relevant ARIES project(s), applicants are expected to complete the ARIES Equality and Diversity Form Applicants - 2027 Entry – Fill in form. One form is required per application, so applicants applying to more than one project must complete a separate form for each.
Please visit www.aries-dtp.ac.uk for further information.
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.