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Master’s in Urban Digital Twin Engineering & 3D City Modelling

Online master's programme of 1,900 hours in urban digital twins and 3D city modelling, structured in 12 modules covering 3D city modelling, data capture, BIM–GIS integration, 3D platforms, urban simulation, real-time data, AI, planning and governance, with OGC CityGML 3.0, OGC 3D Tiles 1.1 and CesiumJS, and an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define urban digital twin architectures.

02

Build semantic 3D models with CityGML and CityJSON.

03

Capture and process LiDAR, photogrammetric and cadastral data.

04

Integrate BIM and GIS models at city scale.

05

Simulate energy, wind, noise and flooding on the twin.

06

Connect real-time data and AI-driven analytics.

07

Govern the digital twin as public infrastructure.

Profile

Who it's for

  • Urban digital twin specialists.
  • 3D GIS analysts.
  • Geospatial data engineers.
  • 3D viewer developers working on web platforms.
  • Digital urban planners.
  • Spatial data infrastructure coordinators.
  • Engineering or science graduates aiming to work in local government.
Standards & tools

What you'll work with

  • OGC CityGML 3.0 (conceptual model for semantic 3D city models)
  • OGC 3D Tiles 1.1 (streaming of massive 3D geospatial content)
  • OGC SensorThings API (interconnecting IoT sensors and observations)
  • ETSI GS CIM 009 (NGSI-LD) (context information management API)
  • Directive 2007/2/EC (INSPIRE) (EU spatial data infrastructure)
  • ISO 16739-1:2024 (IFC 4.3) (Industry Foundation Classes for BIM data exchange)
  • ISO 19650-1:2018 (information management using BIM)
  • CesiumJS
  • FME
  • QGIS
  • CloudCompare
  • ArcGIS Urban
Syllabus

12 modules · 1900 h

  1. 1.1The urban digital twin concept
  2. 1.2Digital twin maturity levels
  3. 1.3Model, data and simulation
  4. 1.4The cases of Singapore, Helsinki and Zurich
  5. 1.5The European Local Digital Twins initiative
  6. 1.6Destination Earth and territorial twins
  7. 1.7Uses in planning and operations
  8. 1.8Governance of the urban twin
  9. 1.9Technical and organisational challenges
  10. 1.10Case study: analysis of a European city's digital twin
  1. 2.1The OGC CityGML 3.0 standard
  2. 2.2Building levels of detail (LOD)
  3. 2.3Automatic LOD2 building modelling
  4. 2.4Urban digital terrain models
  5. 2.5Modelling urban vegetation
  6. 2.6Street furniture and urban networks
  7. 2.7Urban semantics and attributes
  8. 2.8Lightweight encoding with CityJSON
  9. 2.9Geometric validation with val3dity
  10. 2.10Lab: LOD2 model from LiDAR
  1. 3.1Airborne LiDAR and PNOA-LiDAR
  2. 3.2Oblique aerial photogrammetry
  3. 3.3Mobile mapping systems
  4. 3.4Drone-based capture
  5. 3.5High-resolution satellite imagery
  6. 3.6Cadastre data in INSPIRE
  7. 3.7Point cloud classification
  8. 3.8Accuracy and quality control
  9. 3.9Incremental model updating
  10. 3.10Practical exercise: classification of urban point clouds
  1. 4.1BIM models in the urban context
  2. 4.2IFC and its georeferencing
  3. 4.3Converting IFC to CityGML
  4. 4.4Reference systems and projections
  5. 4.5Integrating building and city scales
  6. 4.6BIM-supported planning permits
  7. 4.7OGC standards for 3D data
  8. 4.8Format conversion with FME
  9. 4.9BIM–GIS semantic interoperability
  10. 4.10Workshop: integrating an IFC building into the city model
  1. 5.1Web visualisation with CesiumJS
  2. 5.2The OGC 3D Tiles format
  3. 5.3Game engines for cities
  4. 5.4ArcGIS Urban and planning
  5. 5.5Web viewers for digital twins
  6. 5.6Performance and 3D tiling
  7. 5.7Streaming large models
  8. 5.8Urban virtual reality
  9. 5.9Interfaces for technicians and citizens
  10. 5.10Lab: 3D viewer of a district with CesiumJS
  1. 6.1Rooftop solar potential
  2. 6.2Energy simulation of urban buildings
  3. 6.3Urban wind with CFD
  4. 6.4Urban noise propagation
  5. 6.5Pluvial flooding on the 3D model
  6. 6.6Traffic and pedestrian simulation
  7. 6.7Heat island and comfort
  8. 6.8Visibility and shadow analysis
  9. 6.9Alternative urban planning scenarios
  10. 6.10Practical exercise: photovoltaic potential of a neighbourhood
  1. 7.1Integrating urban sensors
  2. 7.2Context management with FIWARE NGSI-LD
  3. 7.3The OGC SensorThings API standard
  4. 7.4Real-time stream processing
  5. 7.5Operational alerts in the twin
  6. 7.6Integration with municipal systems
  7. 7.7Storage of historical series
  8. 7.8Real-time data quality
  9. 7.9Synchronisation between model and reality
  10. 7.10Case study: operational twin of urban traffic
  1. 8.1Machine learning on 3D data
  2. 8.2Semantic classification of point clouds
  3. 8.3Urban change detection
  4. 8.4Predictive models in the twin
  5. 8.5Optimisation of urban scenarios
  6. 8.6Computer vision with urban imagery
  7. 8.7Mobility graph networks
  8. 8.8Generative AI for urban design
  9. 8.9Bias and ethics in urban analytics
  10. 8.10Lab: change detection with imagery
  1. 9.13D urban planning
  2. 9.2Citizen participation with the twin
  3. 9.3Processing digital permits
  4. 9.4Project impact assessment
  5. 9.5Digital twins of historic heritage
  6. 9.6Urban regeneration and scenarios
  7. 9.7Mobility planning with the twin
  8. 9.8Climate adaptation with twins
  9. 9.9Calculated urban indicators
  10. 9.10Practical exercise: assessment of a new development
  1. 10.1Digital twin governance
  2. 10.2The INSPIRE Directive and spatial data
  3. 10.3Urban data spaces
  4. 10.4Data licensing and ownership
  5. 10.5Privacy in urban models
  6. 10.6Cybersecurity of urban platforms
  7. 10.7Economic sustainability of the twin
  8. 10.8Procurement of digital twins
  9. 10.9Required teams and skills
  10. 10.10Case study: governance model for a city's digital twin
  1. 11.1Twins of underground networks
  2. 11.2Twin of the urban water cycle
  3. 11.3Twin of the electricity distribution network
  4. 11.43D inventory of lighting and street furniture
  5. 11.5Modelling the urban subsurface
  6. 11.6Asset management with the twin
  7. 11.7Urban predictive maintenance
  8. 11.8Integrating networks into the model
  9. 11.9Coordination of street works
  10. 11.10Workshop: 3D model of underground utilities
  1. 12.1Definition of the urban area
  2. 12.2Requirements and use cases
  3. 12.3Data capture and processing
  4. 12.4Semantic 3D model
  5. 12.5Integration of dynamic data
  6. 12.6Simulations and scenarios
  7. 12.7Visualisation platform
  8. 12.8Governance and sustainability
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is CesiumJS, FME, QGIS, CloudCompare and ArcGIS Urban, in free, academic or evaluation versions. Practical work is based on real procedures: 3D modelling of districts from LiDAR, urban simulations on semantic models and real-time sensor integration. Assessment is continuous, module by module, plus the final Master's Thesis.

Standards covered: OGC CityGML 3.0, OGC 3D Tiles 1.1, OGC SensorThings API, ETSI GS CIM 009 (NGSI-LD), Directive 2007/2/EC (INSPIRE), ISO 16739-1:2024 (IFC 4.3), ISO 19650-1:2018.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of OGC CityGML 3.0, OGC 3D Tiles 1.1 and OGC SensorThings API applied to real cases, not as a theoretical annex.
Industry software:CesiumJS, FME, QGIS, CloudCompare and ArcGIS Urban, in free, academic or evaluation versions.
Master's Thesis with defence:An urban digital twin for a defined area, from data capture and semantic 3D modelling to dynamic data, simulations and governance, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Area and use cases
Define the urban area, requirements and use cases for the digital twin.
Data capture and modelling
Process LiDAR point clouds and build a semantic LOD2 model in CityGML.
Dynamic data integration
Connect real-time sensors through SensorThings API and NGSI-LD.
Simulation and visualisation
Run urban simulations and publish the results in a CesiumJS 3D viewer.
Master's Thesis
Integrate the model, data, simulations and governance into a complete urban digital twin, defended before a panel.
Career opportunities

Where this takes you

Urban digital twin specialist3D GIS analystGeospatial data engineer3D viewer developerDigital urban plannerSpatial data infrastructure coordinator
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with CesiumJS, FME, QGIS, CloudCompare and ArcGIS Urban, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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