Artificial intelligence and machine learning
We apply artificial intelligence and machine learning techniques to address environmental challenges
Artificial Intelligence (AI) and Machine Learning (ML) is a rapidly growing and increasingly important research technique, with potential applications in all aspects of research. As more and more environmental data is collected, AI/ML is playing a vital role in analysing, interpreting, and producing meaningful outputs for use by other researchers and wider society.
Through the NERC (Natural Environment Research Council) Earth Observation Data Acquisition and Analysis Service (NEODAAS), PML has a team of AI/ML experts that can consult, advise, train, and provide support on the development and use of AI/ML in environmental research.
Alongside PML’s AI/ML experts, state-of-the-art AI infrastructure has been installed in a bespoke, energy efficient data centre. The MAssive GPU cluster for Earth Observation (MAGEO) is an incredibly powerful computer intended to apply artificial intelligence algorithms to Earth observation (EO) data, to help develop ‘environmental intelligence’ applications that leverage patterns in the natural world. Work already undertaken on the system includes research on wildfires, oil spills, microplastic detection and habitat mapping
PML Project pages
Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME)
Copernicus Evolution: Research for harmonised and Transitional water Observation (CERTO)
NERC Earth Observation and Data Acquisition and Analysis Service (NEODAAS)
S-3 EUROHAB - Sentinel products for detecting EUtROphication and Harmful Algal Bloom events
Smart AUVs for detection and quantification of greenhouse gas seepage in the oceans
Other projectsNew Copernicus Capability for Trophic Ocean Networks (NECCTON)
Smyth T, Moffat D, Tarran G, Sathyendranath S, Ribalet F, Casey J. Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale. Frontiers in Marine Science. 2023 Jul 11. https://doi.org/10.3389/fmars.2023.1191216
Skakala, J., Awty-Carroll, K., Menon, P. P., Wang, K., & Lessin, G. (2023). Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia. Frontiers in Marine Science, 10, 1058837. https://doi.org/10.3389/fmars.2023.105883
Biermann, L; Clewley, D; Martinez-Vicente, V; Topouzelis, K; 2020. Finding Plastic Patches in Coastal Waters using Optical Satellite Data. Scientific Reports.
Graban, S; Dall’Olmo, G; Goult, S; Sauzède, R; 2020. Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient. Optics Express.
Campbell, AM; Racault, MF; Goult, S; Laurenson, A; 2020. Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables. International Journal of Environmental Research and Public Health.
Kerr, T; Clark, JR; Fileman, ES; Widdicombe CE; Pugeault, N; 2020. Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery. IEEE Access.