Reference : R190314
PhD Studentship (3.5 years)
Supervisors : George Vogiatzis and Diego Faria
Key words : Drones, Deep Learning, Recognition, Urban landscapes
Applications are invited for a studentship in the Aston Institute of Urban Technology and the Environment (ASTUTE), funded by the School of Engineering and Applied Science.
The successful applicant will join a cohort of graduate students working on projects across the broader Smart Cities field, and as part of the PhD will receive training and experience in collaborative research, relevant to industry and Smart City planners.
The studentship is offered in collaboration with Geospatial Insight
The position is available to start in October 2019.
This studentship includes a fee bursary to cover the home / EU fees rate, plus a maintenance allowance of £15,009 in 2019 / 20 (subject to eligibility).
Applicants from outside the EU may apply for this studentship but will need to pay the difference between the Home / EU’ and the Overseas’ tuition fees, currently this is £12,573 in 2019 / 20.
As part of the application you will be required to confirm that you have access to this additional funding
One of the major applications of aerial surveillance is the monitoring and detection of change in man-made environments.
The causes of change can be attributed to climate variations, plant and wildlife, human activity and natural disasters, each of which must be monitored for a variety of scientific, economic or regulatory reasons.
Focusing on urban and semi-urban landscapes, there is growing interest in monitoring how these change as a result of construction, for planning purposes, but also as a result of natural disasters (fire, floods, earthquakes) for the purposes of coordinating disaster response or settling insurance claims.
In this project we will work with Geospatial Insight Ltd to investigate the use of drones for the automatic surveillance and change detection in a man-
made environment. Our project goal is a system consisting of a drone with the capability to carry out detailed surveys of a scene of interest and report on changes that may have occurred.
The drone is flown by the scene of interest and identifies locations where change has potentially occurred. This information is then used to perform more detailed surveying around those locations.
The system then produces a report that identifies the changes , classifies them according to predefined criteria (e.g. construction or disaster site) and proceeds to draw high-
level inferences such as completion percentage for construction sites and damage assessment for disaster sites. Such an automated surveying system would represent a real breakthrough : it would significantly advance the scientific goals of Computer Vision and AI but also would revolutionize the change monitoring of urban scenes by lowering the costs and increasing the accuracy.
The successful applicant should have a first class or upper second class honours degree or equivalent qualification in Computer Science, Engineering, Maths or a related discipline as well as excellent programming and analytical / mathematical skills.
A demonstrable interest in Machine Learning / AI is highly desirable.
We would particularly like to encourage applications from women seeking to progress their academic careers. Aston University is committed to the principles of the Athena SWAN Charter, recognised recently by a prestigious Silver Award to EAS, and we pride ourselves on our vibrant, friendly and supportive working environment and family atmosphere.
For formal enquiries about this project contact Dr George Vogiatzis (george-vogiatzis.org) by email at g.vogiatzis aston.ac.uk.
Submitting an application
Details of how to submit your application, and the necessary supporting documents can be found here.
The application must be accompanied by a research proposal statement. An original proposal is not required as the initial scope of the project has been defined, however candidates should take this opportunity to explain how their knowledge and experience will benefit the project and should demonstrate their familiarity with some relevant research literature.
If you require further information about the application process please contact the Postgraduate Admissions team at seasres aston.ac.uk
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