Unit information: Introduction to Geospatial Artificial Intelligence (GeoAI) in 2025/26

Please note: Programme and unit information may change as the relevant academic field develops. We may also make changes to the structure of programmes and assessments to improve the student experience.

Unit name Introduction to Geospatial Artificial Intelligence (GeoAI)
Unit code GEOGM0076
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Zhu
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

None.

Units you must take alongside this one (co-requisite units)

None.

Units you may not take alongside this one

None.

School/department School of Geographical Sciences
Faculty Faculty of Science

Unit Information

This unit provides an advanced introduction to the utilization and development of artificial intelligence (AI) in geography. It will examine fundamentals in geographic information science and basics of AI techniques, promoting the interdisciplinary field of geospatial artificial intelligence (GeoAI). The unit will introduce key concepts, models, and technologies of GeoAI, and discuss its recent advances and applications in addressing geospatial problems, ranging from processing environmental observations to uncovering insights from social sensing. In addition to the utilization of AI in geography, this unit highlights the role geospatial knowledge plays in advancing AI techniques. The unit aims to:

  • Introduce students to some key concepts in geographic information science (e.g. space, place, and time), as well as fundamentals of processing and analysing geospatial data digitally
  • Provide basics of understanding and applying AI techniques in geography, including deep neural networks and knowledge graphs
  • Enable students to responsibly use cutting-edge AI techniques to address real world geospatial problems
  • Raise the awareness of how geospatial is special while using and designing GeoAI methods

Your learning on this unit

Upon successful completion of this unit, students will be able to:

  1. Explain the key concepts of digitally processing and analysing geospatial data and discuss how geospatial data is special in AI
  2. Apply a range of GeoAI methods and tools to analyse different types of geospatial data while addressing real world social and environmental problems
  3. Assess technical limitations and ethical issues of various GeoAI methods

How you will learn

Computer-lab based lectures (mixture of computer practicals and lectures)

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

Throughout the course students will receive formative feedback on code development during the unit seminars.

Tasks which count towards your unit mark (summative):

Report (100%). The assessment tests all the ILOs.

This will elaborate the process of developing GeoAI methods to uncover insights from open geospatial data. This report will be written in a reproducible manner and will include the necessary code for the data analysis and the outputs.

When assessment does not go to plan

Students will be offered an alternative assessment for completion in the summer reassessment period, of a similar format to that of the original submission.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. GEOGM0076).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.