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Master'sInsurance, Risk & Actuarial Data Applications open

Master’s in Geospatial Analytics and Remote Sensing for Risk and Claims Engineering

Online master's programme of 1,900 hours in Geospatial Analytics and Remote Sensing Engineering for Risk and Claims. 12 modules covering geographic information for insurance, remote sensing, exposure and natural hazard mapping, machine learning on imagery, post-event damage assessment, geospatial claims and parametric risk, 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

Manage geographic information for insured portfolios.

02

Process satellite, radar and LiDAR imagery to assess risks.

03

Analyse natural hazards using spatial techniques.

04

Apply deep learning to imagery to detect damage.

05

Automate claims assessment after catastrophic events.

06

Design geospatial indices for parametric insurance.

07

Deploy scalable geospatial platforms in insurance companies.

Profile

Who it's for

  • Geospatial risk analysts looking to apply imagery and spatial data to underwriting and claims.
  • Remote sensing specialists moving into the insurance and reinsurance sector.
  • Geospatial data scientists who want to build damage-detection and risk models.
  • Loss adjusters working with remote tools who want to master post-event imagery.
  • Property underwriters seeking a more precise view of exposure and accumulations.
  • Climate risk consultants working on physical risk and parametric solutions.
  • Engineering or science graduates aiming to enter home and commercial insurance.
Standards & tools

What you'll work with

  • Directive 2007/2/EC (INSPIRE: EU spatial information infrastructure)
  • Regulation (EU) 2021/696 (establishing the Union Space Programme)
  • ISO 19115 (geographic information: metadata)
  • Regulation (EU) 2016/679 (GDPR: personal data protection)
  • Implementing Regulation (EU) 2019/947 (rules for UAS/drone operations)
  • ISO 19157 (geographic information: data quality)
  • Royal Decree 903/2010 (flood risk assessment and management)
  • QGIS
  • Google Earth Engine
  • SNAP (ESA)
  • PostGIS
  • ArcGIS Pro
Syllabus

12 modules · 1900 h

  1. 1.1Geodetic reference systems and ETRS89
  2. 1.2Vector and raster models
  3. 1.3Geocoding of risk portfolios
  4. 1.4Positional accuracy and geocoding errors
  5. 1.5Cartographic and cadastral databases
  6. 1.6Spatial data infrastructures and INSPIRE
  7. 1.7ISO 19115 geographic metadata
  8. 1.8Spatial databases with PostGIS
  9. 1.9OGC web services: WMS and WFS
  10. 1.10Workshop: geocoding a home insurance portfolio
  1. 2.1Electromagnetic spectrum and spectral signatures
  2. 2.2Spatial, spectral and temporal resolution
  3. 2.3Platforms: satellite, aircraft and drone
  4. 2.4The Copernicus programme and Sentinel missions
  5. 2.5Landsat imagery and historical archives
  6. 2.6Synthetic aperture radar (SAR)
  7. 2.7LiDAR and digital elevation models
  8. 2.8Radiometric and atmospheric corrections
  9. 2.9NDVI and NDWI spectral indices
  10. 2.10Lab: processing a Sentinel-2 scene
  1. 3.1Inventory of insured exposures
  2. 3.2Construction characteristics from imagery
  3. 3.3AI-based building and roof detection
  4. 3.4Height and volume from LiDAR
  5. 3.5Valuation of sums insured
  6. 3.6Accumulations by zone and aggregate control
  7. 3.7Proximity risk: fire and vegetation
  8. 3.8Flood risk by location
  9. 3.9Underwriting data enrichment
  10. 3.10Practical exercise: accumulation map of a portfolio
  1. 4.1Flood hazard maps
  2. 4.2National Flood Zone Mapping System (SNCZI)
  3. 4.3Seismic hazard and official maps
  4. 4.4Wildfire risk
  5. 4.5Wind and convective storms
  6. 4.6Hail and its radar detection
  7. 4.7Subsidence and landslides
  8. 4.8Climate change scenarios
  9. 4.9Validation of hazard maps
  10. 4.10Case study: flood hazard in a city
  1. 5.1Spatial autocorrelation and Moran's index
  2. 5.2Interpolation: kriging and IDW
  3. 5.3Spatial regression models
  4. 5.4Density and hotspot analysis
  5. 5.5Network and accessibility analysis
  6. 5.6Spatio-temporal models
  7. 5.7H3 hexagonal indices
  8. 5.8Distributed spatial processing
  9. 5.9Effective cartographic visualisation
  10. 5.10Lab: claims hotspots
  1. 6.1Convolutional networks for aerial imagery
  2. 6.2Semantic segmentation of land cover
  3. 6.3Object detection in orthophotos
  4. 6.4Multi-temporal change detection
  5. 6.5Labelling and training datasets
  6. 6.6Transfer learning and geospatial foundation models
  7. 6.7Spatial accuracy metrics
  8. 6.8Uncertainty in predictions
  9. 6.9Model deployment in the cloud
  10. 6.10Practical exercise: segmentation of roof damage
  1. 7.1Activation of the Copernicus Emergency Management Service
  2. 7.2Rapid flood mapping with SAR
  3. 7.3Fire damage with burn severity indices
  4. 7.4Wind and hail damage with high-resolution imagery
  5. 7.5Drone imagery after the loss event
  6. 7.6Estimating the expected number of claims
  7. 7.7Prioritisation of loss adjustments
  8. 7.8Early reserving after catastrophes
  9. 7.9Communication with affected policyholders
  10. 7.10Case study: damage estimation after a DANA (cut-off low) event
  1. 8.1Validation of the loss location
  2. 8.2Cross-checking with meteorological data
  3. 8.3Remote loss adjustment with imagery
  4. 8.4Automation of claims triage
  5. 8.5Fraud detection with geographic data
  6. 8.6Agricultural claims and remote sensing
  7. 8.7Crop insurance and crop damage
  8. 8.8Integration with claims management systems
  9. 8.9Claims efficiency indicators
  10. 8.10Practical exercise: verification of a hail claim
  1. 9.1Geospatial indices for parametric insurance
  2. 9.2Satellite-derived precipitation
  3. 9.3Vegetation indices for drought
  4. 9.4Design of image-based triggers
  5. 9.5Spatial basis risk
  6. 9.6Downscaled climate scenarios
  7. 9.7Climate stress testing
  8. 9.8Climate disclosure requirements
  9. 9.9Physical risk in investment portfolios
  10. 9.10Workshop: drought index with NDVI
  1. 10.1Cloud geospatial data architectures
  2. 10.2COG, GeoParquet and Zarr formats
  3. 10.3STAC catalogues
  4. 10.4Google Earth Engine and similar platforms
  5. 10.5Batch image processing
  6. 10.6Commercial provider APIs
  7. 10.7Imagery costs and licensing
  8. 10.8Imagery security and privacy
  9. 10.9Integration with BI
  10. 10.10Practical exercise: STAC catalogue of event imagery
  1. 11.1Privacy in high-resolution imagery
  2. 11.2GDPR and geolocated data
  3. 11.3Drone regulation in the EU
  4. 11.4Open data licences
  5. 11.5Responsible use of AI in underwriting
  6. 11.6Risk of exclusion by zone
  7. 11.7Collaboration with the Consorcio de Compensación de Seguros
  8. 11.8Geodata business models
  9. 11.9Return on geospatial investment
  10. 11.10Case study: business case for remote loss adjustment
  1. 12.1Selection of the hazard or process to study
  2. 12.2Review of data sources
  3. 12.3Geospatial methodological design
  4. 12.4Image acquisition and pre-processing
  5. 12.5Analytical or AI model
  6. 12.6Validation with real claims
  7. 12.7Integration into the insurance process
  8. 12.8Cost-benefit analysis
  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 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 is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is QGIS, Google Earth Engine, SNAP (ESA), PostGIS and ArcGIS Pro, in free, academic or evaluation versions. Exercises are based on real procedures: post-flood damage mapping with SAR imagery, roof segmentation in orthophotos with deep learning, and remote claims verification with meteorological data. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Directive 2007/2/EC (INSPIRE), Regulation (EU) 2021/696 establishing the Union Space Programme, ISO 19115, Regulation (EU) 2016/679 (GDPR), Implementing Regulation (EU) 2019/947 on UAS operations, ISO 19157 and Royal Decree 903/2010 on flood risk assessment and management.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of INSPIRE, ISO 19115, ISO 19157 and EU drone and data protection regulations applied to real cases, not as a theoretical annex.
Industry software:QGIS, Google Earth Engine, SNAP (ESA), PostGIS and ArcGIS Pro, in free, academic or evaluation versions.
Master's Thesis with defence:a geospatial analytics project validated against real claims and integrated into the insurance process, 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.

Portfolio geocoding
Geocode a home insurance portfolio and map its accumulations by zone.
Hazard mapping
Model flood hazard for a city and validate the resulting hazard map.
Roof damage segmentation
Train a deep learning model to segment roof damage in orthophotos.
Post-event damage estimation
Estimate damage after a DANA event using SAR imagery and verify a hail claim remotely.
Master's Thesis
Design, validate and cost a geospatial analytics solution for a real insurance process, defended before a panel.
Career opportunities

Where this takes you

Geospatial risk analystRemote sensing specialistGeospatial data scientistLoss adjuster using remote toolsProperty risk underwriterClimate risk consultant
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, Google Earth Engine, SNAP (ESA), PostGIS and ArcGIS Pro, 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, plus a suitable computer 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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