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Getting the most out of deep-sea images for ecological assessment


Project Description

Project Rationale:
Monitoring benthic communities is a fundamental, and in many jurisdictions legal, requirement for the conservation of habitats (e.g. marine protected areas, MPAs), and sustainable development (e.g. assessing impacts of offshore oil and gas and deep-sea mining). Seabed imaging is increasingly used to assess these environments, particularly with the now routine use of autonomous underwater vehicles (AUV, such as Boaty McBoatface) capable of collecting >10,000 images in a day. Although both experts and machine learning are used to extract quantitative information from images, many questions remain regarding the impact of imaging methods on the derived ecological metrics and subsequent conclusions about the status of the environment. How much of what we know about these communities is being influenced by how we monitor them using imagery?

This project will test the effects of the using different image capture parameters (e.g. resolution, survey design), annotation approaches (e.g. human vs. algorithm), and applications of machine learning algorithms (e.g. training data requirements) on the resulting estimated diversity and structure of contrasting benthic communities. This work will establish best practises to optimise the use of image-derived data to monitor communities, both in the UK regulatory context and generalizable for further afield.

Funding Notes

You can apply for fully-funded studentships (stipend and fees) from INSPIRE if you:
Are a UK or EU national.
Have no restrictions on how long you can stay in the UK.
Have been 'ordinarily resident' in the UK for 3 years prior to the start of the project.

Please click View Website for more information on eligibilty and how to apply

References

[1] Blair Thornton, et al., Biometric Assessment of Deep-sea Vent Megabenthos using Multi-Resolution 3D Visual Maps, Deep-sea Research I 116, (2016) 200–219
[2] Durden, J.M., Schoening, T., Althaus, F., Friedman, A., Garcia, R., Glover, A., Greniert, J., Jacobsen Stout, N., Jones, D.O.B., Jordt-Sedlazeck, A., Kaeli, J.W., Koser, K., Kuhnz, L., Lindsay, D., Morris, K.J., Nattkemper, T.W., Osterloff, J., Ruhl, H.A., Singh, H., Tran, M., Bett, B.J., 2016. Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding. In R.N. Hughes, D.J. Hughes, I.P. Smith, A.C. Dale (Eds.), Oceanography and Marine Biology: An Annual Review, Vol. 54 (pp. 1-72): CRC Press.
[3] Piechaud, N., Hunt, C., Culverhouse, P.F., Foster, N.L., Howell, K.L., 2019. Automated identification of benthic epifauna with computer vision. Marine Ecology Progress Series, 615, 15-30.

How good is research at University of Southampton in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 68.62

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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