Funding

Self-funded

Project code

CMP10151026

Department

School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Gelayol Golcarenarenji.

The work on this project will:

  • Develop a pipeline to detect and classify disaster-related objects (e.g., damaged infrastructure, flood zones, trapped humans) from satellite and drone imagery.
  • Employ VLMs for automatic and semantic interpretation of detected scenes.
  • Generate actionable insights (e.g., response priorities, damage severity summaries) for first responders and relief organisations.

Disasters triggered by natural forces or human actions pose significant threats to societies worldwide, often leading to cross-border destruction and humanitarian crises. Each year, floods, earthquakes, storms, and explosions result in the loss of countless lives and inflict severe damage on infrastructure and economies. To support disaster response and recovery efforts, remote sensing (RS) technology, which enables large-scale Earth observation from afar, has seen widespread application. It plays a crucial role in assessing and  evaluating damages. In light of the urgent nature of disaster management, integrating artificial intelligence into these processes is essential to improve the speed and accuracy of decision-making.

Object detection techniques applied to remote sensing imagery allow automated identification and localisation of critical features such as damaged buildings, flooded areas, and blocked roads, enabling rapid damage assessment. Furthermore, vision-language models (VLMs) provide a powerful tool to enhance situational awareness and support effective communication among responders and decision-makers. Together, these AI-driven approaches promise to significantly enhance disaster management workflows by combining precise visual analysis with contextual understanding. Hence, the aim of this project is to develop an integrated framework that leverages remote sensing imagery, object detection, and vision-language models to enhance disaster damage assessment and response. 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in computer science or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

You should have computer programming knowledge using Python, Pytorch or Tensorflow.

 

 

How to apply

We’d encourage you to contact Dr Gelayol Golcarenarenji (gelayol.golcarenarenji@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: CMP10151026