Postgraduate research 

Geospatial Data Science PhD

  • PhD: 3-4 years full-time; 6-8 years part-time; Thesis of Max 80,000 words

Research projects

Self-funded projects


Indicative Data Science: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)

This project will implement a crowdsourcing-based approach to create accurate 3D models from the free to use and globally available data of Global Navigation Satellite Systems (GNSS).

The effects of urban features, such as buildings and trees, on GNSS signals, i.e. signal blockage and obstruction, and attenuation, will help to recognise the shape, size, and materials of urban features, through the application of statistical, machine learning (ML) and artificial intelligence (AI) techniques.

The use of freely accessible raw GNSS data, which can be accessed on any current Android device, will enable the production of up to date 3D models at no or low cost, of particular value in developing regions where these models are not currently available.


GNSS is the most widely used positioning technique because of free-to-use, privacy-preserving, and globally available signals. However, GNSS signals can be blocked, reflected and/or attenuated by objects, e.g. trees, buildings, walls and windows. While blockage, attenuation and reflection of GNSS signals are common in urban canyons and indoors, making the positioning unreliable, inaccurate or impossible, the affected received signals can act as an indicator of the structure of the surrounding environments.

This means, for example, if the signals are blocked or attenuated, then the size and shape of the obstacles or the type of media/material the signals have gone through or been reflected by can be understood. This needs the precise locations of satellites, and the receiver, and also predicted signal strength level at each location and time.

The crowdsource-based framework, i.e. a mobile app for data capture and a web mapping application for upload of GNSS raw data, will allow the project to have well-distributed data both in space and time. This will ultimately lead to higher quality (more spatially and temporally accurate, complete, precise) 3D models.

However due to the complexity of data, as neither the receiving mobile devices nor the broadcasting satellites are fixed, some novel data mining techniques, based on already existing statistical, ML, and AI techniques, need to be developed during this fellowship. They will handle the high volume, the velocity of change, and the complexity of the spatio-temporal GNSS raw data with high levels of veracity. The spatio-temporal patterns will be used for creating and updating the 3D models of cities at a high level of detail (LoDs), i.e. approximating the façade and the building materials, e.g. windows, from which the signals are reflected or have gone through. The 3D models will feed into 3D-mapping aided GNSS positioning (and integrated with other signals e.g. WiFi) which can ultimately provide more continuous and accurate GNSS positioning in urban canyons and indoors.



Developing methods embracing challenges of new forms of data

We develop theoretical and applied solutions to the challenges of 'new forms of data' such as missingness and biases. Our Indicative Data Science project looks at developing solutions based on the mindset that considers the bias, missingness, or low quality as a useful source of data to make inference about the underlying reasons for the missingness and bias.

In the era of big data, open data, social media, and crowdsourced data when 'we are drowning in data', gaps and unavailability, representativeness, and bias issues associated with them may indicate some hidden problems or reasons of biases and missingness. These novel solutions allow us to understand the data, society, and cities better.  



Colouring Glasgow

Colouring Glasgow is a knowledge exchange platform that provides open statistical data about the city's buildings and the dynamic behaviour of the stock. We're working to collate, collect, generate, verify over fifty types of data and to visualise many of these datasets.

Our information comes from many different sources. As we are unable to vouch for data accuracy, we are experimenting with how to present data sources, how data are edited over time, and how to ask for data verification, to help you to check reliability and judge how suitable the data are for your intended use. Your help in checking and adding data is very much appreciated.



Intelligent Navigation Services

This extremely multi-disciplinary research theme looks at different aspects of navigation including positioning and localisation, path finding and routing algorithms, sense of direction, human computer interaction, cognitive navigation, intelligent mobility, and artificial intelligence.



Sensor fusion and Signals of Opportunity

We look at different positioning signals and state-of-the-art sensor data to understand the mobility and movement of people in a non-intrusive way, model occupancy of buildings and energy consumption, better urban planning and use of space, and more recently for contact tracing and social distancing purposes.



Explainable AI (XAI) for geospatial data modelling

Machine learning and artificial intelligence (ML/AI) have been increasingly used to model geospatial phenomena. However, ML/AI models are often considered as 'black box’ approaches that their internal mechanisms cannot be understood easily by humans. This raises problems around model transparency, bias, discrimination, trustworthiness, and accountability, all of which have strong technical and ethical implications. This project focuses on the fundamental aspects of XAI and aims to develop novel methods to improve the explainability of AI-based geospatial modelling.



Understanding Polycentric Urban Systems with Geospatial Data Science

A polycentric urban system refers to a system of multiple physically independent but connected urban agglomerations. It has been widely observed as an empirical reality and envisioned by policymakers as a sustainable development tool worldwide. This project aims to develop an innovative methodological framework to measure polycentric urban systems at multiple scales across different geographical contexts.



Built environment and collective human behaviors

The proliferation of geospatial data and the advancement of computational methods allow us to delineate the built environment and observe collective behaviors in fine detail. Against this backdrop, this project aims to understand the relationship between the built environment and collective human behaviors by combining geospatial data and computational methods.



Our Geospatial Data Science programme is suitable for students wishing to pursue a PhD and undertake innovative research in a wide range of subjects which aligns to one of our Geospatial Data Science themes, including:

  • geostatistics
  • geospatial data quality
  • new forms of geospatial data such as crowdsourced data to help improve our understanding and location-based services to the societies.

Further information about Geospatial Data Science

Study options

Phd 3-4 6-8


Entry requirements

English language requirements

For applicants whose first language is not English, the University sets a minimum English Language proficiency level.

International English Language Testing System (IELTS) Academic module (not General Training)

  • 6.5 with no subtests under 6.0
  • Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements using a single test
  • IELTS One Skill Retake accepted.

Common equivalent English language qualifications accepted for entry to this programme:

TOEFL (ibt, my best or athome)

  • 79; with Reading 13; Listening 12; Speaking 18;Writing 21
  • Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements , this includes TOEFL mybest.

Pearsons PTE Academic

  • 59 with minimum 59 in all subtests
  • Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements using a single test.

Cambridge Proficiency in English (CPE) and Cambridge Advanced English (CAE)

  • 176 overall, no subtest less than 169
  • Tests must have been taken within 2 years 5 months of start date. Applicants must meet the overall and subtest requirements using a single test.

Oxford English Test

  • Oxford ELLT 7
  • R&L: OIDI level no less than 6 with Reading: 21-24 Listening: 15-17
  • W&S: OIDI level no less than 6

Trinity College Tests

Integrated Skills in English II & III & IV: ISEII Distinction with Distinction in all sub-tests.

University of Glasgow Pre-sessional courses

Tests are accepted for 2 years following date of successful completion.

Alternatives to English Language qualification

  • Degree from majority-English speaking country (as defined by the UKVI including Canada if taught in English)
    • students must have studied for a minimum of 2 years at Undergraduate level, or 9 months at Master's level, and must have complete their degree in that majority-English speaking country and within the last 6 years
  • Undergraduate 2+2 degree from majority-English speaking country (as defined by the UKVI including Canada if taught in English)
    • students must have completed their final two years study in that majority-English speaking country and within the last 6 years

For international students, the Home Office has confirmed that the University can choose to use these tests to make its own assessment of English language ability for visa applications to degree level programmes. The University is also able to accept UKVI approved Secure English Language Tests (SELT) but we do not require a specific UKVI SELT for degree level programmes. We therefore still accept any of the English tests listed for admission to this programme.

Pre-sessional courses

The University of Glasgow accepts evidence of the required language level from the English for Academic Study Unit Pre-sessional courses. We also consider other BALEAP accredited pre-sessional courses:

Fees and funding



  • UK: £4,786
  • International & EU: £30,240

Prices are based on the annual fee for full-time study. Fees for part-time study are half the full-time fee.

Irish nationals who are living in the Common Travel Area of the UK, EU nationals with settled or pre-settled status, and Internationals with Indefinite Leave to remain status can also qualify for home fee status.

Alumni discount

We offer a 20% discount to our alumni on all Postgraduate Research and full Postgraduate Taught Masters programmes. This includes University of Glasgow graduates and those who have completed Junior Year Abroad, Exchange programme or International Summer School with us. The discount is applied at registration for students who are not in receipt of another discount or scholarship funded by the University. No additional application is required.

Possible additional fees

  • Re-submission by a research student £540
  • Submission for a higher degree by published work £1,355
  • Submission of thesis after deadline lapsed £350
  • Submission by staff in receipt of staff scholarship £790

Depending on the nature of the research project, some students will be expected to pay a bench fee (also known as research support costs) to cover additional costs. The exact amount will be provided in the offer letter.



The vibrancy of our research environment derives from our large body of postgraduate students.

We take an integrated approach to study at Glasgow, bringing together internationally leading expertise in physical and human geography, geology and geomatics.

Our postgraduate students benefit from many fieldwork opportunities, ranging from short day excursions close to Glasgow to longer residential field trips, which may involved overseas travel.

The School has close links with industry. We arrange many guest speakers and there are also informal opportunities to meet people from industry at open events. Projects may be carried out in conjunction with industry.

You will be part of a Graduate School which provides the highest level of support to its students.

The overall aim of our Graduate School is to provide a world-leading environment for students which is intellectually stimulating, encourages them to contribute to culture, society and the economy and enables them to become leaders in a global environment.

We have a diverse community of over 750 students from more than 50 countries who work in innovative and transformative disciplinary and interdisciplinary fields. An important part of our work is to bring our students together and to ensure they consider themselves an important part of the University’s academic community.

Being part of our Graduate School community will be of huge advantage to you in your studies and beyond and we offer students a number of benefits in addition to exceptional teaching and supervision, including:

  • A wide-ranging and responsive research student training programme which enables you to enhance your skills and successfully complete your studies.
  • Mobility scholarships of up to £4000 to enable you to undertake work in collaboration with an international partner.
  • A diverse programme of activities which will ensure you feel part of the wider-research community (including our biannual science slam event).
  • A residential trip for all new research students.
  • The opportunity to engage with industry-partners through training, placements and events.
  • Professionally accredited programmes.
  • Unique Masters programmes run in collaboration with other organisations.
  • State-of-the-art facilities including the James Watt Nanofabrication Centre and the Kelvin Nanocharacterisation Centre.
  • Highly-rated support for international students.

Over the last five years, we have helped over 600 students to complete their research studies and our students have gone on to take up prestigious posts in industries across the world.


How to apply

Identify potential supervisors

All Postgraduate Research Students are allocated a supervisor who will act as the main source of academic support and research mentoring. You may want to identify a potential supervisor and contact them to discuss your research proposal before you apply. Please note, even if you have spoken to an academic staff member about your proposal you still need to submit an online application form.

You can find relevant academic staff members with our staff research interests search.

Gather your documents

Before applying please make sure you gather the following supporting documentation:

  1. Final or current degree transcripts including grades (and an official translation, if needed) – scanned copy in colour of the original document.
  2. Degree certificates (and an official translation, if needed): scanned copy in colour of the original document.
  3. Two references on headed paper and signed by the referee. One must be academic, the other can be academic or professional. References may be uploaded as part of the application form or you may enter your referees contact details on the application form. We will then email your referee and notify you when we receive the reference.  We can also accept confidential references direct to, from the referee’s university or business email account.
  4. Research proposal, CV, samples of written work as per requirements for each subject area.
Apply now

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