Master’s in Advanced Vision/Geomatics Engineering (Beyond Mobility)
Online master's programme of 1,900 hours in advanced vision and geomatics engineering beyond mobility. 12 modules covering reference systems and geodesy, optical remote sensing, SAR and InSAR, photogrammetry and LiDAR, deep learning for geospatial imagery, 3D point cloud analysis, advanced spatial analysis and spatial data infrastructures under the INSPIRE Directive, ISO 19115-1 and ISO 19157-1, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Apply reference systems and geodetic transformations.
Process optical and radar satellite imagery.
Generate photogrammetric products and digital terrain models.
Develop deep learning models for geospatial imagery.
Analyse LiDAR point clouds to extract information.
Apply geomatics to environmental and industrial problems.
Publish geographic information in line with INSPIRE and OGC standards.
Who it's for
- Geomatics engineers expanding into AI-based image analysis.
- Remote sensing specialists working with optical and radar data.
- Geospatial data scientists building models at scale.
- Computer vision engineers applying their skills to aerial and satellite imagery.
- GIS analysts looking to add photogrammetry and deep learning to their toolkit.
- Photogrammetry engineers working with drones and LiDAR.
- Engineering or science graduates looking to enter the environmental sector.
What you'll work with
- Directive 2007/2/EC (INSPIRE, infrastructure for spatial information in the EU)
- ISO 19115-1 (geographic information metadata)
- ISO 19157-1 (geographic data quality)
- ISO 19111 (referencing by coordinates)
- Implementing Regulation (EU) 2019/947 (operation of unmanned aircraft systems)
- OGC API – Features (web access to geospatial vector data)
- Regulation (EU) 2016/679 (GDPR)
- QGIS
- Google Earth Engine
- ESA SNAP
- Agisoft Metashape
- PDAL
12 modules · 1900 h
1.1Convergence of computer vision and geomatics1.2Applications in agriculture, the environment and heritage1.3Infrastructure, mining and energy1.4Airborne, satellite and terrestrial sensors1.5Spatial and temporal scales1.6The geospatial data value chain1.7The Copernicus market and services1.8Ethics and privacy in Earth observation1.9Current technical challenges1.10Case study: needs analysis for a geospatial service
2.1Ellipsoids, datums and the geoid2.2ETRS89 and ITRF systems2.3Map projections and UTM2.4Coordinate transformations2.5Ellipsoidal and orthometric heights2.6GNSS and RTK positioning2.7The PROJ library and EPSG codes2.8Georeferencing errors2.9Geospatial metadata according to ISO 191152.10Lab: coordinate transformations with PROJ
3.1Radiation physics and spectral signatures3.2Sentinel-2 and Landsat multispectral sensors3.3Radiometric and atmospheric corrections3.4Spectral indices for vegetation and water3.5Satellite hyperspectral imagery3.6Spatial, spectral and temporal resolution3.7Cloud masks3.8Image time series3.9Cloud processing platforms3.10Practical: spectral indices with Sentinel-2
4.1Principles of SAR4.2Geometry and distortions4.3SAR polarimetry and decompositions4.4InSAR interferometry and phase4.5Ground deformation with PS-InSAR4.6Sentinel-1 and SAR missions4.7Change detection with SAR4.8Flood mapping4.9Processing with SNAP4.10Lab: subsidence map with InSAR
5.1Epipolar geometry and orientation5.2Aerial triangulation and bundle adjustment5.3Structure from Motion5.4Digital surface and terrain models5.5True orthophoto generation5.6Airborne and terrestrial LiDAR5.7Point cloud classification5.8Drone photogrammetry5.9Geometric quality control5.10Workshop: orthomosaic and DSM from drone imagery
6.1Particularities of satellite imagery6.2Scene classification6.3Semantic segmentation of land cover6.4Object detection in aerial imagery6.5Instance segmentation of buildings6.6Geospatial foundation models6.7Self-supervised learning in remote sensing6.8Labelling and public datasets6.9Spatial validation and data leakage6.10Practical: land-cover segmentation with U-Net
7.1Data structures and octrees7.2Point cloud filtering and registration7.3Geometric plane segmentation7.4PointNet and successor networks7.5Semantic segmentation of point clouds7.6Extraction of buildings and vegetation7.73D city modelling and CityGML7.8Scanning for heritage7.9Managing large volumes7.10Lab: LiDAR point cloud classification with deep learning
8.1Vector and raster models8.2Spatial databases with PostGIS8.3Network analysis8.4Spatial statistics and autocorrelation8.5Interpolation and kriging8.6Multi-temporal change analysis8.7Geospatial data cubes8.8Distributed raster processing8.9Cartographic visualisation and semiology8.10Workshop: data cube with xarray and STAC
9.1Forest monitoring and deforestation9.2Precision agriculture9.3Water quality and coastal areas9.4Wildfires and burnt areas9.5Glaciers and the cryosphere9.6Biodiversity and habitats9.7Emissions and air quality9.8Climate change and adaptation9.9Sustainability indicators9.10Case study: monitoring burnt areas
10.1Inspection of power lines10.2Mining and volume control10.3Monitoring dams and slopes10.4Bridge inspection with drones10.5Cadastral management and parcel updating10.6Renewable energy and site selection10.7Documenting cultural heritage10.8Insurance and damage assessment10.9Emergency management10.10Practical: visual inspection of infrastructure with drones
11.1Requirements of the INSPIRE Directive11.2ISO 19100 series standards11.3OGC WMS, WFS and WCS services11.4OGC APIs and STAC11.5Open data and licences11.6Drone regulation in the EU11.7Privacy in high-resolution imagery11.8Geographic data quality according to ISO 1915711.9Interoperability and metadata11.10Case study: publishing an INSPIRE-compliant geospatial service
12.1Defining the geospatial problem12.2Selecting sensors and data12.3Pre-processing and georeferencing12.4Developing the analysis model12.5Validation with ground truth12.6Producing cartographic products12.7Publication and services12.8Conclusions and applicability12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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, Google Earth Engine, ESA SNAP, Agisoft Metashape and PDAL, in free, academic or evaluation versions. Real procedures are studied: mapping deformation with InSAR, drone photogrammetry for inspection, and land-cover classification with deep learning. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Directive 2007/2/EC (INSPIRE), ISO 19115-1, ISO 19157-1, ISO 19111, Implementing Regulation (EU) 2019/947 (UAS), OGC API – Features and Regulation (EU) 2016/679 (GDPR).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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, ESA SNAP, Agisoft Metashape and PDAL, 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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