Geospatial Data Science & AI

Entry requirements

2.2 Hons (or non-UK equivalent) in a relevant field such as Geography, Earth or Environmental Science

We may also consider applicants with a background in Mathematics, computing science, engineering or Physics.

Any other subjects with relevant work experience

Entry without standard academic qualifications, or with technical qualifications in geomatics for those with significant practical experience in related employment, will be considered on an individual basis. Study of this program would consist a significant use of Computers and IT.

Months of entry

September

Course content

Our MSc equips you with advanced geospatial, analytical, programming, and AI/Machine Learning (ML) skills. Become adept at working with geospatial data across environmental, urban, and socio-technical domains. Develop strong foundations in GIS, cartography, spatial statistics, and remote sensing. Then become more specialised in emerging areas such as geospatial artificial intelligence (GeoAI), ML applications for Earth system problems, and big geospatial data analytics. Teaching is highly applied and research-led. You'll use real-world datasets, modern software and tools (e.g., Python, QGIS, and ArcGIS) and reproducible workflows. The MSc project with academic or industry partners allows you to build a strong portfolio for future study or employment.

Why this Programme?

• GeoAI-first curriculum – One of the first programmes in the UK to foreground GeoAI, including vision-language models and trustworthy AI practice, grounded in core geospatial concepts.
• Strong geospatial foundations – Build robust skills in GIS, cartography, spatial statistics, geospatial fundamentals, and Earth observation/remote sensing, so that AI methods are always underpinned by sound spatial thinking.
• Flexible pathways and options – Tailor your learning through options such as Big GeoData Analytics, Remote Sensing of the Environment, Environmental Statistics, Web and Mobile Mapping, Geospatial Data Infrastructures and Land Administration, and Applied GIS, with suggested pathways in Geospatial Data Science and Computational Environmental Sciences.
• Hands-on, project-based learning – Learn primarily through labs, computer practicals, and project work using real geospatial datasets, with continuous assessment that mirrors professional practice, including analytical reports, programming assignments, presentations.
• Modern tools and infrastructures – Gain practical experience with Python, open-source and commercial GIS, modern data infrastructures, and reproducible workflows that are highly valued by employers and research organisations.
• Addressing recognised skills gaps – The programme directly responds to national and international calls for graduates who can integrate environmental or geoscience knowledge with advanced data management, spatial analysis and visualisation, and environmental statistics.
• Supportive, research-rich environment – Learn from staff who are actively engaged in Geospatial Data Science, GeoAI, Earth Observation/Remote Sensing, and other environmental applications, within a School that holds an Athena Swan Silver Award and a strong commitment to inclusive, student-centred active learning.

Information for international students

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

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