The Global Centre for Risk and Innovation (GCRI)
Risk Academy, a Nexortium programmeRisk Science and MethodsDepartment of Geospatial and Risk Data Science
GIS, remote sensing, exposure mapping, GeoAI and the accuracy of what a map shows
A map is a model with an accuracy that can be measured.
risksacademy.comrisk-sciencediscipline
Twin figures for the central Apennines: L'Aquila, Amatrice and Norcia, counted from its layers at their stated dates.
For Geospatial analysis, Earth observation and GeoAI: the faculty's twin of L'Aquila on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and - zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.
The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).
The discipline teaches GIS foundations and coordinate reference systems, remote sensing and image analysis, radar flood mapping, exposure mapping, GeoAI methods and their validation, spatial statistics and autocorrelation, cartography for risk and the Earth observation missions that supply the data, on the Copernicus elevation model and the USGS products of the Apennine twin. A graduate can build a spatial layer from open data, assess its accuracy against reference data and present it as a map whose limits are stated.
This area covers the geospatial methods of risk analysis: GIS foundations, remote sensing and image analysis, radar flood mapping, exposure mapping, GeoAI, spatial statistics and cartography, with accuracy assessment and the critical review of what maps and models can show. It is taught in the department of Geospatial and Risk Data Science, Faculty of Risk Science and Methods, through 3 courses with it as primary discipline and 3 more that also teach it.
10 subtopics, taught across its courses and labs.
Recent developments in geospatial analysis, Earth observation and GeoAI, each with its source; sources checked on 16 October 2026. Summaries are short readings of the source; follow the link for the full account.
News and research as they are published, per discipline, with the faculty's other tools.
The newest research, preprints, news and humanitarian reports for Geospatial analysis, Earth observation and GeoAI, each with its source and date, load here. Open the research and news hub
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
Courses with this as their primary discipline. Open courses link to the catalogue.
| Lab | What you do in it | Courses using it |
|---|---|---|
| Exposure lab | Overlay people and assets with hazard footprints | 5 |
| GeoAI lab | Apply Earth observation and machine learning to open imagery | 4 |
| Map lab | Read hazard, exposure and boundaries on real maps | 3 |
| Geo quiz | Check spatial reasoning on where hazards and systems meet | 3 |
These courses count in their own primary discipline and also teach this one; they do not count towards its achievement as primary courses.
Cited as places where the subject is practised; the institutions named are sources here, and Risk Academy hosts every course.
Nexus Foundry system pages describe what each system and its rooms are designed to do; they are not a service on offer, and studying here gives no access to them.
| Term | Definition |
|---|---|
| Raster and vector data | Grids of cells and geometric features, the two main forms of spatial data. |
| Accuracy assessment | Comparing a map with reference data to estimate its errors. |
| Coordinate reference system | The definition that ties coordinates to locations on the Earth. |
| GeoAI | The use of machine learning on spatial and Earth observation data. |
| Spatial autocorrelation | The tendency of nearby locations to have similar values. |
The catalogue lists each course with its outcomes, labs, hours and level.