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About the Project
Although humans cannot prevent natural disasters in most cases, timely responses can play a critical role in disaster relief and lifesaving. Rapid and accurate building damage assessment is required in humanitarian assistance and disaster response to carry out life-saving efforts. Automatic information extraction from high-resolution satellite sensor images collected from disaster-affected areas is imperative under time-critical situations and has the potential to greatly facilitate post-disaster assessment, but this remains an extremely challenging task for the state-of-the-art machine learning algorithms.
This PhD project will explore in-depth the power of cutting-edge deep learning techniques in image understanding, segmentation, classification and change detection, and develop an accurate, reliable, automated solution to facilitate the challenging building damage assessment task based on paired pre- and post-disaster high-resolution satellite sensor images.
This project is part of an EPSRC-funded New Investigator Award expected to advance the state-of-the-art machine learning and remote sensing research with a focus on explainable artificial intelligence and computer vision. This project is in collaboration with research partners at the Lancaster University, University of Bristol, and University of Sheffield.
Supervisors: Dr Xiaowei Gu and Professor Ferrante Neri
Entry requirements
Open to any UK or international candidates. Starting in July 2025. You will need to meet the minimum entry requirements for our PhD programme.
The candidate should be highly motivated and can engage in collaboration with good oral and written communication skills. Previous research experience in machine learning, deep learning and/or computer vision is essential. A keen interest on remote sensing is highly desirable.
How to apply
Applications should be submitted via the Computer Science PhD programme page. In place of a research proposal, you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.
Funding Notes
Fully and directly funded for this project only.
Standard stipend (£19,237 p.a. 2024/25 rates); full home or overseas tuition fees (as applicable); a research, training and support grant of up to £3,000 over the project. Funding available for 3.5 years (42 months).
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