PhD Studentship - Blue eyes: New Tools for Monitoring Coastal Environments using Remotely Piloted Aircraft and Machine Learning (MACKIEWICZM_U18iNERC)

University of East Anglia - School of Computing Sciences

Start Date: October 2018

No. of positions available: 1

Supervisor: Dr Michal Mackiewicz, Secondary Supervisors: Prof Graham Finlayson, Dr Julie Bremner, Dr Tony Dolphin

Project description:

Remote sensing of coastal environments is a rapidly evolving field, with remotely piloted aircraft (RPA) showing growing potential for mapping and tracking the distribution, health and dynamics of coastal features such as vegetation, shellfish, birds, mammals, geomorphology and even litter. Their advantages over traditional environmental monitoring techniques is in their speed of deployment and ability to deliver ultra-high resolution 2-D and 3-D images of entire landscapes and the environmental features within them, which can be used to accurately map and objectively assess even subtle or early-day changes in the physical environment and in biodiversity. However, processing the resulting data is a substantial bottleneck to progress. The tools developed in this project will allow a faster and more effective interpretation of images, vastly improving the utility of the coastal change tracking technology.

The methods employed to detect and classify the environmental features in the images are pivotal to the success of RPA use in marine ecosystems. Traditional image processing techniques use pixel-based supervised classification, but the super-high resolution of RPA images can render such methods cumbersome and ineffective - increasing costs and causing delays in data production. This studentship aims to develop bespoke tools for RPA image analysis using ‘deep learning’, a complex machine learning technique that has recently proven to provide a step-change in a number of computer vision applications. Fusion of the now available 3-D information with the traditional RGB imaging will be one of the main focal points of the project as it promises significant improvements in performance. The algorithm development will require a large dataset of annotated imagery for training and the expert knowledge on the image appearance which are available in Cefas.  The student will also contribute to the design and deployment of Cefas RPA flights as the research progresses.


The successful candidate will

  • Be affiliated to the NEXUSS CDT which provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for environmental sciences, alongside personal and professional development.
  • Have extensive opportunities to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners.
  • be registered in the School of Computing Sciences in the Graphics, Vision and Speech laboratory, and
  • Receive training in all areas relevant to the project including computer vision, machine learning as well as Matlab and Python programming.
  • Spend periods of time at Cefas, Lowestoft and University of Southampton in order to familiarize with the images and the ecological aspects of the project.

Person specification: Any numerate discipline.

Minimum entry requirement is 2:1

Funding notes: This NERC Industrial Case studentship is in partnership with Cefas funded for 3 years 8 months. An annual stipend (in 2017/18 the stipend is £14,553) will be available to the successful candidate who meets the UK Research Council eligibility criteria. These requirements are detailed in the RCUK eligibility guide which can be found at In most cases UK and EU nationals who have been ordinarily resident in the UK for 3 years prior to the start of the course are eligible for a full-award. Other EU nationals may qualify for a fees only award.

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