PhD Study : Gaining a better understanding of our Planet through Deep Learning-based Data Analytics

Summary

Earth observation satellite imagery and electromagnetic signals are widely used for capturing land usage, deformation and climate change over time on a regional and global scale. The aim of the project is to help observe and better understand the evolution of the land environment and inform experts so greater resilience to geohazards can be produced.  When combined, the imagery and signals contain the degree of detail to allow environmental changes to be monitored. A series of studies have been conducted on change detection from satellite data at Ulster University, including the development of data analytics algorithms for detecting seismic anomalies from the satellite and electromagnetic data. Additionally, environmental factors have been extracted from satellite images for understanding vector-borne diseases.

Currently, with the advance of sensor technology, spatial, temporal and spectral resolutions of satellite imagery and electromagnetic signal have been significantly advanced to allow even finer grain investigations, but this generates greater volumes of data that needs to be analysed and classified. The project will develop and integrate detection algorithms on multi-source, multi-resolution and multi-spectral satellite data at various levels of detail to monitor change detection.

The research will investigate the data analytic techniques that are underpinned by machine learning with a particular focus on using deep learning technologies. The proposed project will develop algorithms, which will be used to detect abnormalities in the environment found in the data, such as, regions prone to air pollution or opportunities to improve sustainable development of energy.

The project will involve:

1) the use signal analysis tools to extract pixel and object-based features from satellite imagery to identify relevant geographical features;

2) combine the satellite imagery and wavelet features detected to develop techniques using the state of the art machine (deep structured, such as TensorFlow) learning approaches; and

3) evaluate the developed techniques for a selected domain, such as, studying earthquakes, energy, vector-borne diseases, monitoring air pollution or pest control in agriculture to allow experts to better understand our Planet.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• Publications record appropriate to career stage
• A comprehensive and articulate personal statement
• Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living