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Master’s in GeoAI & Dynamic Mapping

Online master's programme of 1,900 hours in GeoAI and dynamic mapping: telemetry, risk maps and spatio-temporal prediction. 12 modules covering geospatial data, remote sensing, spatial analysis, geospatial machine and deep learning, point clouds, real-time mapping and GeoAI platforms, with an assigned technical tutor.

Duration12–14 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Manage geospatial data with appropriate reference systems and standards.

02

Process optical and radar satellite imagery.

03

Apply spatial analysis and statistics.

04

Develop geospatial machine learning and deep learning models.

05

Process point clouds and 3D city models.

06

Publish dynamic maps and real-time services.

07

Apply GeoAI to natural hazards, cities and infrastructure.

Profile

Who it's for

  • GeoAI specialists looking to consolidate their skills.
  • Remote sensing analysts moving into deep learning.
  • Geospatial engineers.
  • Geospatial data scientists.
  • Cartography technicians who want to work with dynamic, AI-driven maps.
  • GIS developers.
  • Engineering or science graduates aiming to work in public administration.
Standards & tools

What you'll work with

  • Directive 2007/2/EC (INSPIRE spatial data infrastructure)
  • ISO 19115-1 (geographic metadata)
  • ISO 19157-1 (geographic data quality)
  • OGC API – Features (web access to vector features)
  • ISO 19111 (referencing by coordinates)
  • Regulation (EU) 2019/947 (drone operations)
  • OGC CityGML 3.0 (3D city models)
  • QGIS
  • Google Earth Engine
  • ArcGIS Pro
  • PostGIS
  • TorchGeo
Syllabus

12 modules

  1. 1.1Reference systems and projections
  2. 1.2ETRS89 and UTM
  3. 1.3Vector and raster models
  4. 1.4GeoPackage, GeoTIFF and COG formats
  5. 1.5Spatial data infrastructures
  6. 1.6The INSPIRE Directive
  7. 1.7IGN and Copernicus data sources
  8. 1.8Geographic data quality
  9. 1.9Geospatial metadata
  10. 1.10Case study: data catalogue for a municipality
  1. 2.1Principles of remote sensing
  2. 2.2Multispectral optical sensors
  3. 2.3Sentinel-2 and Landsat
  4. 2.4Sentinel-1 synthetic aperture radar
  5. 2.5NDVI and NDWI spectral indices
  6. 2.6Atmospheric corrections
  7. 2.7Very high-resolution imagery
  8. 2.8Drones and photogrammetry
  9. 2.9PNOA airborne LiDAR
  10. 2.10Lab: vegetation index with Sentinel-2
  1. 3.1Proximity and overlay analysis
  2. 3.2Spatial autocorrelation and Moran's index
  3. 3.3Kriging and IDW interpolation
  4. 3.4Geographically weighted regression
  5. 3.5Hot spot analysis
  6. 3.6Networks and accessibility
  7. 3.7Digital elevation models
  8. 3.8Applied geostatistics
  9. 3.9Scale and the MAUP problem
  10. 3.10Practical exercise: accessibility analysis with QGIS
  1. 4.1Land use classification
  2. 4.2Random Forest on imagery
  3. 4.3Spatial sampling and validation
  4. 4.4Autocorrelation and data leakage
  5. 4.5Spatial feature engineering
  6. 4.6Image time series
  7. 4.7Change detection
  8. 4.8Google Earth Engine
  9. 4.9Accuracy assessment
  10. 4.10Lab: crop map with Earth Engine
  1. 5.1Convolutional networks for remote sensing
  2. 5.2Semantic segmentation with U-Net
  3. 5.3Object detection in aerial imagery
  4. 5.4Building and road extraction
  5. 5.5Super-resolution
  6. 5.6Optical–radar fusion
  7. 5.7Geospatial foundation models
  8. 5.8Benchmark datasets
  9. 5.9Labelling and weak supervision
  10. 5.10Practical exercise: building segmentation with TorchGeo
  1. 6.1LiDAR processing
  2. 6.2Point cloud classification
  3. 6.3PointNet and 3D networks
  4. 6.4Digital terrain and surface models
  5. 6.5CityGML 3D city models
  6. 6.6Photogrammetry and structure from motion
  7. 6.7Urban digital twins
  8. 6.83D change detection
  9. 6.9Visualisation with Cesium
  10. 6.10Lab: urban LiDAR classification
  1. 7.1Vector tile maps
  2. 7.2OGC WMS, WFS and API Features services
  3. 7.3Streaming of geolocated data
  4. 7.4Georeferenced IoT data
  5. 7.5Mobility and GPS trajectories
  6. 7.6Dynamic heat maps
  7. 7.7PostGIS databases
  8. 7.8H3 spatial indexes
  9. 7.9Scaling in the cloud
  10. 7.10Practical exercise: dynamic map with PostGIS and MapLibre
  1. 8.1Flood mapping with radar
  2. 8.2Wildfires and burn severity
  3. 8.3Drought and water stress
  4. 8.4Landslides with InSAR
  5. 8.5Copernicus Emergency Management
  6. 8.6Spatial air quality
  7. 8.7Climate change and monitoring
  8. 8.8Biodiversity and habitats
  9. 8.9Early warning
  10. 8.10Case study: rapid flood mapping
  1. 9.1Cadastre and map updating
  2. 9.2Urban planning
  3. 9.3Infrastructure inventory
  4. 9.4Deformation monitoring
  5. 9.5Utility networks
  6. 9.6Rooftop solar energy
  7. 9.7Urban heat islands
  8. 9.8Mobility and transport
  9. 9.9BIM-GIS integration
  10. 9.10Workshop: solar potential of a neighbourhood
  1. 10.1Large-scale imagery pipelines
  2. 10.2STAC and image catalogues
  3. 10.3Cloud-native Zarr and COG formats
  4. 10.4Distributed processing with Dask
  5. 10.5Geospatial model serving
  6. 10.6Geospatial MLOps
  7. 10.7Storage and compute costs
  8. 10.8Reproducibility
  9. 10.9Reference architectures
  10. 10.10Practical exercise: STAC pipeline for Sentinel imagery
  1. 11.1Privacy and location data
  2. 11.2Copernicus data licences
  3. 11.3Drone use and EASA regulations
  4. 11.4Bias in geospatial models
  5. 11.5Responsible cartographic communication
  6. 11.6Public procurement of mapping
  7. 11.7Geospatial business models
  8. 11.8Open science
  9. 11.9Industry trends
  10. 11.10Case study: data service for insurers
  1. 12.1Problem definition and study area
  2. 12.2Data acquisition
  3. 12.3Preprocessing and harmonisation
  4. 12.4GeoAI model design
  5. 12.5Training and validation
  6. 12.6Map production
  7. 12.7Publication as a service
  8. 12.8Evaluation of results
  9. 12.9Thesis 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 each 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, Google Earth Engine, ArcGIS Pro, PostGIS and TorchGeo, in free, academic or evaluation versions. Testing is studied on real procedures for satellite image classification, map validation with field data and multi-temporal change detection. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2007/2/EC (INSPIRE), ISO 19115-1, ISO 19157-1, OGC API – Features, ISO 19111, Regulation (EU) 2019/947, OGC CityGML 3.0.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the INSPIRE Directive, ISO 19115-1 and ISO 19157-1 applied to real cases, not as a theoretical annex.
Industry software:QGIS, Google Earth Engine, ArcGIS Pro, PostGIS and TorchGeo, in free, academic or evaluation versions.
Master's Thesis with defence:a complete GeoAI project, from data acquisition and model design to map production and publication as a 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.

Data catalogue
Build a geospatial data catalogue for a study area using INSPIRE-compliant sources.
Image classification
Produce a crop or land use map with Earth Engine and assess its accuracy.
Deep learning extraction
Segment buildings from aerial imagery with TorchGeo.
Dynamic map service
Publish a dynamic map with PostGIS and MapLibre fed by real-time data.
Master's Thesis
Develop a complete GeoAI project, from data acquisition to publication as a service, defended before a panel.
Career opportunities

Where this takes you

GeoAI specialistRemote sensing analystGeospatial engineerGeospatial data scientistCartography technicianGIS developer
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer able to run 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, Google Earth Engine, ArcGIS Pro, PostGIS and TorchGeo, 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 demonstrable 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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