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Science Topic

Oil pollution

PML routinely monitors areas to detect if oil pollution occurs, so timely action can be taken.

PML have developed a Machine Learning algorithm to automatically detect oil slicks from satellite data. By routinely downloading and processing satellite data after every overpass and applying this automated oil slick detection it is possible to monitor areas for possible slicks and alert government authorities so that timely action can be taken.

The technique was developed in the Malacca strait, one of the busiest shipping lanes in the world, where oil spills are known to have disastrous consequences. It is currently being used as part of the EO4SD project to monitor oil spills off the coast of West Africa. Through this monitoring, regular discharges off the coast of Ghana have been identified. The commercial service developed in EASOS is being offered to government and industry through PML Applications.
 

Front cover of the "Satellite monitoring of oil spills" brochure

Satellite monitoring of oil spills

Download this informative brochure to find out more about how PML is using advanced techniques to support detection and clean-up of oil spills.

Download the PDF (4.3MB)


Support oil pollution research  

 

Resources and Links

EASOS Marine Watch case study

Capabilities

  • Operational Satellite Data Processing
  • Automated Oil Spill Detection
  • Expertise in image interpretation

Selected publications

Kurekin, AA; Loveday, BR; Clements, O; Quartly, GD; Miller, PI; Wiafe, G; et al. 2019. Operational Monitoring of Illegal Fishing in Ghana through Exploitation of Satellite Earth Observation and AIS Data. Remote Sensing.

People who work in this area of research

Dr Elizabeth C. Atwood

Earth Observation Data Analyst
liat6/22/2024 4:12:03 PM@pml.ac.uk

Dr Dan Clewley

Senior Research Software Engineer
dac6/22/2024 4:12:03 PM@pml.ac.uk

William Jay

Geospatial Research Software Engineer
wja6/22/2024 4:12:03 PM@pml.ac.uk

Dr Andrey Kurekin

Marine Earth Observation Scientist
anku6/22/2024 4:12:03 PM@pml.ac.uk

Dr Mark Warren

Remote sensing scientist
mark16/22/2024 4:12:03 PM@pml.ac.uk