Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

• To appreciate and apply the concepts and methods associated with contemporary 3D geospatial data generation pipelines that underpin the creation of digital twins.
• To develop advanced understanding of the Internet of Things (IoT) and the integration of IoT streaming data into geospatial digital twins.
• To appreciate the creation of all stages of geospatial digital twin creation and operation.

Outline Of Syllabus

The syllabus follows the data flow line for the creation and utilisation of geospatial digital twins from data capture, data modelling through to the representation of digital twins and their integration with streaming data. It begins with photogrammetric computer vision, followed by an exploration of other 3D data capture methods and tools. Students will investigate laser scanning, including mobile mapping techniques, contemporary Artificial Intelligence (AI) approaches, and learn about point cloud processing and analysis. The module covers Building Information Modelling (BIM) and GeoBIM, providing insights into underlying digital twin concepts and applications. Geospatial representation of digital twins is a key focus, along with the standards and practices associated with BIM such as IFC, COBIE and CityGML. Students will also study the Internet of Things (IoT), sensor networks, sensor data types and formats, and their applications.Finally, the module addresses time-series analysis and the use of Machine Learning and AI-based models for time-series analytics.

Intended Knowledge Outcomes

On successful completion of the module, students will be able to:

• describe the core principles and methods of photogrammetric computer vision;
• comprehend different 3D reality capture pipelines, including those adopting the latest Geospatial-AI tools and methods;
• appreciate how 3D models can be created, represented and processed, including using AI;
• appreciate the pertinent standards and data formats associated with geospatial digital twins;
• describe how data can be linked to geospatial digital twins;
• appreciate time-series based AI models.

Intended Skill Outcomes

On Successful completion of the module students will be able to:

• critically evaluate and implement open and proprietary algorithms and tools for photogrammetric computer vision;
• conduct primary data acquisition, processing and analysis using laser scanning and mobile mapping technologies;
• assess alternative methods of 3D reality capture using Geospatial-AI skills;
• create and integrate 3D building models into GIS;
• manage, visualise and analyse time-series data;
• develop and apply AI-based models to time-series data.

Teaching Rationale And Relationship

Lectures convey the core concepts, theories, and methods. Practicals enable the principles introduced in lectures to be put into practice, learned and assimilated through hands-on examples. The practicals have been developed to enable students to work independently and the practicals before each coursework submission will enable students to develop their submission. Tutorial clinics provide the opportunity for students to ask questions arising post-practicals and before submission deadlines.

Assessment Rationale And Relationship

The coursework extends the practical work to provide an opportunity to consolidate understanding and obtain feedback. The closed-book, timed exam provides the means for a student to individually demonstrate their cumulative knowledge and understanding gained as needed in future careers and the workplace.

The formative quiz provides a means to test their understanding mid module.

General Notes

N/A