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Master’s in GeoAI & Dynamic Mapping Engineering

Online master's programme of 1,900 hours in GeoAI and dynamic mapping engineering. 12 modules covering geospatial data engineering, spatial statistics and learning, geospatial deep learning, automatic map updating, real-time mapping, mobility analysis, GeoAI for risk and the environment, natural language and geography, and deployment under the INSPIRE Directive, ISO 19157-1 and the GDPR, with 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

Apply statistical and learning methods that account for spatial dependence.

02

Build scalable geospatial data pipelines.

03

Develop deep learning models for imagery and networks.

04

Automate map updating.

05

Publish dynamic and real-time maps.

06

Analyse mobility data while respecting privacy.

07

Produce maps in line with quality standards and ethical criteria.

Profile

Who it's for

  • Geospatial data scientists applying AI to territorial problems.
  • GeoAI engineers building production models and services.
  • Digital cartographers automating map production.
  • Mobility analysts working with GPS and phone data.
  • Web map developers building dynamic, real-time viewers.
  • Territorial risk analysts producing hazard and vulnerability maps.
  • Engineering or science graduates looking to enter the public sector.
Standards & tools

What you'll work with

  • Directive 2007/2/EC (INSPIRE, infrastructure for spatial information in the EU)
  • ISO 19157-1 (geographic data quality)
  • Regulation (EU) 2016/679 (GDPR)
  • ISO 19115-1 (geographic information metadata)
  • Implementing Regulation (EU) 2023/138 (high-value datasets)
  • OGC API – Processes (web processing of geospatial data)
  • Regulation (EU) 2024/1689 (AI Act)
  • QGIS
  • PostGIS
  • Google Earth Engine
  • TorchGeo
  • MapLibre GL JS
Syllabus

12 modules · 1900 h

  1. 1.1Artificial intelligence and geographic information science
  2. 1.2Spatial dependence and heterogeneity
  3. 1.3Geospatial data: vector, raster and trajectories
  4. 1.4Static versus dynamic mapping
  5. 1.5GeoAI use cases
  6. 1.6Tool ecosystem
  7. 1.7Spatial biases in models
  8. 1.8Ethics and surveillance
  9. 1.9Current research in GeoAI
  10. 1.10Case study: designing a dynamic mapping service
  1. 2.1GeoJSON, GeoParquet and COG formats
  2. 2.2PostGIS spatial databases
  3. 2.3R-tree and H3 spatial indexes
  4. 2.4Open data sources and OpenStreetMap
  5. 2.5Satellite data and STAC catalogues
  6. 2.6Sensor data and IoT networks
  7. 2.7GPS trajectories and mobility
  8. 2.8Integrating heterogeneous data
  9. 2.9Data quality and updating
  10. 2.10Lab: ingestion pipeline with GeoParquet and H3
  1. 3.1Spatial autocorrelation and Moran's I
  2. 3.2Geographically weighted regression
  3. 3.3Kriging and spatial Gaussian processes
  4. 3.4Bayesian spatio-temporal models
  5. 3.5Spatial cross-validation
  6. 3.6Trees and boosting with spatial variables
  7. 3.7Spatial clustering with DBSCAN
  8. 3.8Hot spot detection
  9. 3.9Interpolation with machine learning
  10. 3.10Practical: spatial prediction with block validation
  1. 4.1Convolutional networks for satellite imagery
  2. 4.2Segmentation of buildings and roads
  3. 4.3Multi-temporal change detection
  4. 4.4Image super-resolution
  5. 4.5Graph networks on road networks
  6. 4.6Transformers for spatio-temporal data
  7. 4.7Geospatial foundation models
  8. 4.8Location embeddings
  9. 4.9Transfer between regions
  10. 4.10Lab: road extraction with deep segmentation
  1. 5.1Change detection in cartographic databases
  2. 5.2Automatic building extraction
  3. 5.3Automatic cartographic generalisation
  4. 5.4Data conflation
  5. 5.5Automatic quality control
  6. 5.6Incremental map updating
  7. 5.7Collaborative mapping and validation
  8. 5.8High-definition maps
  9. 5.9Cartographic production workflows
  10. 5.10Workshop: updating buildings from orthophotos
  1. 6.1Real-time maps from data streams
  2. 6.2Vector tiles and dynamic styles
  3. 6.3Map servers
  4. 6.4Web visualisation with MapLibre and deck.gl
  5. 6.5Mapping events and alerts
  6. 6.6Updatable risk maps
  7. 6.7Territorial digital twins
  8. 6.8Performance with large volumes
  9. 6.9Cartographic user experience
  10. 6.10Practical: web viewer for real-time data
  1. 7.1Cleaning and map matching
  2. 7.2Detecting stops and trips
  3. 7.3Human mobility patterns
  4. 7.4Estimating origin-destination matrices
  5. 7.5Trajectory prediction
  6. 7.6Privacy in location data
  7. 7.7Mobile phone data
  8. 7.8Mobility and urban planning
  9. 7.9Flow visualisation
  10. 7.10Lab: GPS trajectory analysis with MovingPandas
  1. 8.1Flood mapping
  2. 8.2Wildfire risk
  3. 8.3Landslides and geological hazards
  4. 8.4Air quality and dispersion models
  5. 8.5Urban heat islands
  6. 8.6Land use and planning
  7. 8.7Biodiversity and habitats
  8. 8.8Copernicus emergency services
  9. 8.9Uncertainty quantification in maps
  10. 8.10Case study: dynamic flood risk map
  1. 9.1Geoparsing and place-name extraction
  2. 9.2Geocoding and reverse geocoding
  3. 9.3Gazetteers and toponymic databases
  4. 9.4Social media as sensors
  5. 9.5Natural language queries over maps
  6. 9.6LLMs and geospatial agents
  7. 9.7Geographic knowledge graphs
  8. 9.8Evaluating geolocation
  9. 9.9Limitations and biases
  10. 9.10Workshop: extracting locations from news
  1. 10.1Cloud-native geospatial architectures
  2. 10.2Distributed processing with Dask and Apache Sedona
  3. 10.3OGC API services
  4. 10.4MLOps for geospatial models
  5. 10.5Containers and scaling
  6. 10.6Image processing costs
  7. 10.7Monitoring produced maps
  8. 10.8Security and access control
  9. 10.9Interoperability between geospatial services
  10. 10.10Practical: geospatial inference service with OGC API Processes
  1. 11.1Obligations under the INSPIRE Directive
  2. 11.2ISO 19100 geographic information standards
  3. 11.3Data quality according to ISO 19157
  4. 11.4EU high-value datasets
  5. 11.5Privacy and the GDPR in geolocation
  6. 11.6The AI Act and surveillance
  7. 11.7Open data licences
  8. 11.8Accountability in risk maps
  9. 11.9Spatial justice and territorial equity
  10. 11.10Case study: ethical assessment of a vulnerability map
  1. 12.1Defining the territorial problem
  2. 12.2Data sources and preparation
  3. 12.3Modelling with GeoAI techniques
  4. 12.4Spatial validation of results
  5. 12.5Dynamic cartographic production
  6. 12.6Deploying the service
  7. 12.7Quality and impact assessment
  8. 12.8Conclusions and transfer to the territory
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine the programme with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is QGIS, PostGIS, Google Earth Engine, TorchGeo and MapLibre GL JS, in free, academic or evaluation versions. Real procedures are studied: automatic updating of building maps, dynamic flood risk maps, and analysis of mobility trajectories. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2007/2/EC (INSPIRE), ISO 19157-1, Regulation (EU) 2016/679 (GDPR), ISO 19115-1, Implementing Regulation (EU) 2023/138 (high-value datasets), OGC API – Processes and Regulation (EU) 2024/1689 (AI Act).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the INSPIRE Directive, ISO 19157-1 and the GDPR applied to real cases, not as a theoretical annex.
Industry software:QGIS, PostGIS, Google Earth Engine, TorchGeo and MapLibre GL JS, in free, academic or evaluation versions.
Master's Thesis with defence:a GeoAI solution to a real territorial problem, deployed as a dynamic mapping service, 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.

Define the territorial problem
Frame the problem and identify and prepare the data sources.
Model with GeoAI
Build spatial learning or deep learning models for the task.
Validate spatially
Validate the results with spatial cross-validation and quality checks.
Deploy the dynamic map
Produce the dynamic cartography and deploy it as a web service.
Master's Thesis
Present the quality and impact assessment, defended before a panel.
Career opportunities

Where this takes you

Geospatial Data ScientistGeoAI EngineerDigital CartographerMobility AnalystWeb Map DeveloperTerritorial Risk Analyst
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 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 QGIS, PostGIS, Google Earth Engine, TorchGeo and MapLibre GL JS, 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 programme 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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