NERC GW4+ DTP PhD studentship: Remote sensing and DEEP learning for early warning of WATER quality hazards (DeepWater)
University of Exeter - College of Life and Environmental Science
|Funding for:||UK Students, EU Students|
|Funding amount:||£14,296 per annum|
|Placed on:||13th October 2016|
|Closes:||6th January 2017|
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NERC GW4+ DTP PhD studentship Remote sensing and DEEP learning for early warning of WATER quality hazards (DeepWater)
Main supervisor: Dr Chunbo Luo (College of Engineering, Mathematics and Physical Sciences, University of Exeter)
Current satellite sensors allow global monitoring of water resources at an unprecedented optical and spatial resolution, paving the way for operational monitoring of potential hazards from space. Of particular interest is the potential value of satellite observations during and following episodic events such as heavy wind and rain, which may lead to harmful algal blooms, sewage overflow and poor visibility.
The overarching objectives of this project are to develop image segmentation and object-based satellite image processing techniques for cases where water quality issues are evident. A successful monitoring solution would provide hazard warning information to the affected companies and end users.
The first such case concerns mapping river plumes and their sphere of influence. Tracing river plumes from their source to the furthest extent, visible as an optical and/or radar signature, directly informs commercial (e.g. aquaculture) and recreational (e.g. diving, fishing, surfing) use of potential risks in the event of strong storm runoff.
The second case focuses on mapping of dynamic features such as potentially harmful algal and cyanobacterial blooms, and relatively stable features such as shallow areas (bottom visibility), and floating vegetation, in coastal and inland water systems. Object oriented mapping would classify these optically dominant structures and create a novel approach to spatial binning on satellite imagery.
The project will seek to tackle the challenge by exploiting the new generation of high resolution satellite imagery (Sentinel-1 (radar), Sentinel-2 (optical), and Landsat optical missions) and numerous advanced computing techniques which have not yet been applied in remote sensing of water bodies , e.g. deep learning, sub-pixel level endmember extraction methods, local parallelisation and distributed processing techniques etc.
This project provides an excellent opportunity to explore a multidisciplinary topic. The High Performance Computing and Networking, Artificial Intelligence Group and Centre for Water Systems at Exeter, Plymouth Marine Laboratory (Earth Observation Science and Applications group) and Pixalytics Ltd (Satellite imagery expert, CASE partner) create an ideal team with complimentary expertise for the proposed research. The funded student will benefit from collaboration and training opportunities through the EU H2020 EOMORES project and by working closely with the CASE partner.
This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus six Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/
At least 4 fully-funded studentships that encompass the breadth of earth and environmental sciences are being offered to start in September 2017 at Exeter. The studentships will provide funding for a stipend which is currently £14,296 per annum for 2016-2017, research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.
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South West England