Presencial en Glasgow
12 months months full-time 24 months months part‑time
September
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.
Graduates of the MSc Geospatial Data Science & AI are equipped to work at the interface of geospatial technologies, data analytics and visualisation, and AI. You will be able to (1) design and implement data-driven solutions to geospatial problems, (2) manage, analyse, and map complex environmental and urban datasets, and (3) communicate geospatial insights effectively to technical and non-technical audiences.
Typical roles include:
- Geospatial / GeoAI Data Scientist
- Earth Observation or Remote Sensing Analyst
- Spatial Data Engineer or GIS Developer
- Urban or Transport Analytics Specialist
- Environmental or Climate Risk Modeller
- Location Intelligence / Geo-visualisation Specialist
Graduates find opportunities in environmental consultancies, government agencies, national mapping organisations, transport and utilities companies, tech firms and start-ups, as well as finance, insurance and climate/fintech sectors where location-based risk and asset modelling are increasingly important. The programme also provides a strong foundation for PhD study in geospatial data science, GeoAI, Earth observation, environmental informatics and related fields.
The MSc in Geospatial Data Science and AI is delivered predominantly through computer-based practical classes, laboratories and workshops, supported by lectures, seminars and labs/tutorials. Learning is strongly applied and interactive, with most courses assessed through coursework along the learning journey.
Full-time students normally complete 120 credits of taught courses over two semesters, followed by a 60-credit MSc project in the summer. Part-time students spread their taught courses over two years and normally complete the project in the third year.
Semester one
- Academic and Professional Skills for GES PGT (10 credits)
- Geospatial Fundamentals (20 credits)
- Introduction to Statistics for Environmental Analysis (10 credits)
- Principles of GIS (10 credits)
- Principles of Cartographic Design & Production (10 credits)
These courses introduce core concepts in GIS, mapping, spatial data processing, and statistical thinking, facilitating you to develop the academic and professional skills needed for MSc-level study.
Semester two
Core courses (30 credits)
- Introduction to Geospatial Artificial Intelligence (GeoAI) – explores foundation models, spatially explicit AI models, representation learning, and applications of deep learning for various geospatial tasks.
- Machine Learning Applications for Earth Systems Problems – develops your understanding and skills of ML techniques based on environmental and Earth-system data.
Optional courses (30 credits)
You will choose 60 credits from the following courses:
- GES_Spatial Data Analytics (10 credits)
- Remote Sensing of the Environment (10 credits)
- Environmental and Ecological Statistics (Level M) (10 credits)
- Web and Mobile Mapping (10 credits)
- Geospatial Data Infrastructures and Land Administration (10 credits)
- Applied GIS (10 credits)
These options allow you to shape your degree toward one of two indicative pathways, or to create a bespoke route:
- Geospatial Data Science track – emphasising geospatial data infrastructures, applied GIS, and web/mobile mapping.
- Computational Environmental Science track – emphasising remote sensing, environmental statistics, and geospatial data infrastructures.
Summer - MSc Project
You will complete an independent research project or industry-aligned dissertation on a topic of your choice, supervised by a member of academic staff and often using real datasets from external partners. The project allows you to consolidate the knowledge and skills gained throughout the programme, and to produce a substantial piece of work to showcase to employers or support applications for PhD study.
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