Geo-Aware Graph Learning for Transferable Flood Mapping from Earth Observation Data (GRAFT)

About the Project

Project Description

Flood mapping from Earth observation (EO) data is a critical input to disaster risk management and climate resilience planning. However, machine learning models for flood detection and flood susceptibility mapping often struggle to generalise when applied to new geographical regions. Differences in hydrology, land cover, terrain, and climate introduce strong spatial heterogeneity, leading to severe performance degradation under geographical transfer. This limitation poses a major barrier to scalable, globally deployable flood monitoring systems.

Aims and Methods

The aim of this PhD project is to develop geo-aware graph learning models that explicitly incorporate geographical context as an inductive bias to improve cross-regional generalisation in flood mapping from EO data. The project will investigate how spatial relationships and geographical attributes can be modelled within graph-based machine learning frameworks to distinguish transferable flood-related patterns from region-specific environmental characteristics.

Methodologically, the project will represent spatial entities (e.g. grid cells or spatial units) as nodes in a graph, with edges encoding geographical proximity or hydrological connectivity. Multi-source EO data (e.g. optical, thermal, land cover, and vegetation indices) will be integrated as node features. The student will design and evaluate geo-aware graph neural networks in which geographical information conditions message passing or representation learning, rather than being treated as a simple input feature. Models will be evaluated under explicit geographical transfer settings, where training and testing regions differ substantially in environmental properties.

Deliverables (indicative)

• A proof-of-concept geo-aware graph learning model for cross-regional flood mapping that explicitly conditions representation learning on geographical context.
• Quantitative evaluation of model generalisation under geographical distribution shift, using standardised cross-region training and testing protocols.
• At least three conference or journal publications in remote sensing or machine learning venues.
• Reproducible geo-transfer datasets and modelling pipelines, including curated multi-region EO data, standardised geographical train–test splits, and end-to-end workflows for graph construction, training, and robustness evaluation, enabling fair comparison of transferable flood mapping models and supporting deployment in previously unseen regions.

Keywords

Flood mapping; Earth observation; Graph neural networks; Geographical transfer; Spatial machine learning; Climate resilience

Contact for information on the project

Dr Oktay Karakuş – karakuso@cardiff.ac.uk

How to Apply

This project is accepting applications all year round, for self-funded candidates.

Mode of Study: Full-time or part-time

Please submit your application viaComputer Science and Informatics - Study - Cardiff University

In the funding field of your application, indicate “I am applying for a self-funded PhD in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants must demonstrate English language proficiency. Students who do not have English as a first language must prove this by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component. A full list of accepted qualifications is available here: https://www.cardiff.ac.uk/study/international/english-language-requirements/postgraduate

If you are interested, please contact Dr Oktay Karakuş (karakuso@cardiff.ac.uk) sending your CV in the first instance. The application process requires you to develop an individual research proposal jointly with the supervision team, which builds on the information provided in this advert.

Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below.

Please submit your application viaComputer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information:

• In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal. Your research proposal should not exceed 2000 words, including references and bibliography.
• A personal statement (as part of the university application form, or as a separate attachment, if you prefer).
• A CV. Guidance on CVs for a PhD position can be found on the FindAPhD website.
• Qualification certificates and Transcripts - original and English translation, if applicable.
• References x 2 which should be academic references. Please note you need to provide the reference documents as part of your application.
• Proof of English language (if applicable).

Interview– If the application meets all ofthe entrance requirements listed above, you will be invited to an interview.

Funding Notes

This project is offered for self-funded students only, or those with their own sponsorship or scholarship award. Where applicable, candidates will be required to cover the cost of a student visa, the healthcare surcharge, and any other costs of moving to the UK to study. These costs will not be covered by the School of Computer Science and Informatics.