nexustandards.com/space · 2 taught
Space and Earth Observation
NRS-M3 Exposure and asset intelligence; NRS-T6 Earth observation and space systems risk
2standards1discipline
Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output
Risk Academy, led by the Nexus Consortium · Risk Science and Methods · Department of Geospatial and Risk Data Science
GIS, remote sensing, exposure mapping, GeoAI and the accuracy of what a map shows
Geospatial analysis, Earth observation and GeoAI
A map is a model with an accuracy that can be measured.
- Courses
- 1 course open
- Levels
- L1 Introductory, L2 Intermediate, L3 Advanced
- Labs
- Exposure lab, GeoAI lab, Map lab, Geo quiz
- GIS foundations
- Remote sensing and image analysis
- Radar flood mapping
- Exposure mapping
- GeoAI methods
- Map accuracy and validation
- Spatial statistics
- Cartography for risk
- Earth observation sensors and missions
- Disaster monitoring from space
02Maps
Build a twin of any place
Search any place or open the world; the layers and generators follow geospatial analysis, Earth observation and GeoAI on the planet map, the globe and in 3D.
Risk Science and MethodsAny placeTwin builder
- Terrain 24 to 2,805 m
- 3 reference earthquakes, M6.2 to M6.6
- Earthquakes M4.5 and above, past week: read live when the 3D twin opens
- USGS ShakeMap intensity contours: read live when the 3D twin opens
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.
2 live layers, checked 17 Oct 2026
Places4
Layers4
iCredits
- Terrain Surface height in whole metres on the plan's grid of cells about 455 by 605 metres; the box rises from 24 m in the Tiber valley to 2,805 m on the Gran Sasso. Produced using Copernicus WorldDEM-30 © DLR e.V. 2010–2014 and © Airbus Defence and Space GmbH 2014–2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. read 2026-10-17. Estimate: A digital surface model: trees and roofs are in the heights. Relief exaggeration is printed on the stage.
- Reference earthquakes The three reference main shocks (L'Aquila 2009, Amatrice 2016, Norcia 2016): origin, magnitude, depth, time and review status from the USGS detail record, the hypocentre drawn below the ground. U.S. Geological Survey Earthquake Hazards Program. read 2026-10-17. Estimate: Reported solutions as reviewed by the USGS; magnitudes and depths carry the USGS stated uncertainties, which the event page links rather than restates.
- Earthquakes M4.5 and above, past week (live) Reviewed and automatic seismic solutions of the past week inside the box, with magnitude and depth, read from the world feed and filtered to the box in your browser; the distribution fitter reads the same world feed for its sample. U.S. Geological Survey; reported detections, automatic solutions preliminary; never a warning. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is printed with it. Estimate: Reported detections; automatic solutions are preliminary; never a warning.
- USGS ShakeMap intensity contours The ShakeMap MMI contours of each reference event draped on the terrain, read from the cont_mmi.json file the event's detail record names: what one realisation of the sampler's field looked like. U.S. Geological Survey ShakeMap and U.S. Geological Survey PAGER (preliminary, automatically generated; see the USGS disclaimers), with the product version and time. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is printed with it. Estimate: Preliminary and automatically generated; PAGER levels are USGS estimates with uncertainty, never a warning or a loss figure of the Academy.
Why this place: One box of about 82 by 77 kilometres holds the 2009 L'Aquila (M6.3), 2016 Amatrice (M6.2) and Norcia (M6.6) earthquakes, the Gran Sasso massif and the normal-fault basins between them. It is the reference case for every method the faculty teaches: magnitude-frequency fitting on a dense catalogue, return periods, probabilistic hazard, model validation, and risk communication after the L'Aquila trial of 2012, which turned on how probabilities were communicated. Data quality: Copernicus DEM GLO-30 covers the relief in two tiles, the USGS detail records of the three main shocks are reviewed, and the live feed is open. It connects to the Quantitative risk analysis and statistics, Catastrophe, exposure and vulnerability modelling, Uncertainty and decision making, Risk communication and risk literacy and Geospatial analysis, Earth observation and GeoAI disciplines and to the spatial intelligence pages of the ecosystem sites.
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 that can draw it, once its data and code have been checked.
The boundaries, names and designations shown do not imply any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. Countries: Natural Earth (public domain).
03Live map
Earth observation in Argentina
True-colour imagery, land cover, vegetation, snow and sea ice, over Argentina.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 1
Earth observation
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Earth observation, framed on Argentina.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (10 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
- Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Oct 2026
- Sea ice concentration: GHRSST MUR, NASA JPL PO.DAAC · 12 Oct 2026
04The subject
The subject, methods and measures and debates and bodies
The field and its scope
For analysts, modellers and mapmakers who use geographic information, satellite observation and spatial machine learning to locate hazards, exposure and damage, and who must judge what a map can and cannot show.
Quantities and units
Methods and their limits
Each method is listed with what it assumes and where it fails.
What the field debates
Bodies that set practice, and the data they publish
Bodies and standards are named so learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
05The method
From a radar image to the people in the water: a flood map, its accuracy sample and the area the sample corrects
06At a glance
At a glance, with 5 related topics
Geospatial analysis, Earth observation and GeoAI at a glance
risksacademy.comrisk-sciencediscipline
- Open, 1
- In preparation, 2
- Also taught here, 3
- Subtopics, 10
Nexus Standards it teaches and relies on
The Nexus Standards the discipline teaches toward, grouped by master standard, with the disciplines that teach each.
nexustandards.com/digital-ai · 1 taught
Digital, Cyber and AI
NRS-T4 Sensing and measurement networks risk
1standard1disciplinenexustandards.com/data · 1 taught
Data and Evidence
NRS-M22 Risk data
1standard1disciplinenexustandards.com/open-infrastructure · 1 taught
Open Infrastructure and Developers
NRS-A8 AI-ready risk data and model packaging
1standard1discipline
Nexus Standards at the AcademyThe Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review.
Research and tools
Research and preprints as they are published, by discipline, with the faculty's other tools.
The hub lists the newest research and preprints for Geospatial analysis, Earth observation and GeoAI, each with its source and date. Open the research hub
The methods it teaches
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
Methods and practice
- Earth observation and GIS3 courses
- AI and GeoAI2 courses
- Hazard, exposure, vulnerability and capacity assessment1 course
- Risk data governance1 course
Hazard families
- Riverine flood2 courses
- AI and algorithmic failure1 course
- Coastal flood and storm surge1 course
What is changing now
Recent developments in geospatial analysis, Earth observation and GeoAI, each with its source; sources checked on 16 October 2026.
Courses in this discipline
Courses with this as their primary discipline.
Courses from connected disciplines that also teach this field
Each course has its primary discipline elsewhere and teaches this field as part of its scope.
07Labs
How the subject is practised in labs
| 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 |
08Console
How Geospatial analysis, Earth observation and GeoAI is taught, as a process you can run
Press Play to run this process from this page's own data.
09Learning
Learning, practice and connections
Achievements in this discipline
Pathways that teach it
Where the subject is practised
- The Global Centre for Risk and Innovation (GCRI Canada)Space and Earth ObservationSpatial temporal evidence, EO in water and agriculture, Earth observation sensors, Disaster monitoring
- FoundryMaps and GIS
Field platforms of the ecosystem where the subject is studied and applied.
The two Foundry systems where it is practised
- FoundryNXS-EOP systemEarth observation and geospatial computationAcquisition room, Change room, Maps room
- NXS-GRX systemStatistical-geospatial products
Foundry system pages describe what each system and its rooms are designed to do; the courses teach the methods these systems apply.
Disciplines it depends on
- Faculty of Technology and Digital SystemsSpace systems, space weather and navigationThe discipline covers the satellites, ground segments and services that provide positioning, navigation.
- Faculty of Earth and Environmental SciencesFood systems and agricultureFood systems risk concerns whether people can reliably obtain safe and nutritious food, from soils.
Across the ecosystem
10Take part
Take part in Risk Science and Methods
Start from who you are.
11References
Core references and where to begin
Start with Tobler, then Olofsson, then Anselin.
12Questions
Questions, answers and key terms
Is study here free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the courses in geospatial analysis, Earth observation and GeoAI?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.
Key terms
Term
Raster and vector data
Grids of cells and geometric features, the two main forms of spatial data.
Term
Accuracy assessment
Comparing a map with reference data to estimate its errors.
Term
Coordinate reference system
The definition that ties coordinates to locations on the Earth.
Term
GeoAI
The use of machine learning on spatial and Earth observation data.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
Risk Academy
Related
Around Geospatial analysis, Earth observation and GeoAI
Geospatial analysis, Earth observation and GeoAI
Start with an open course
The catalogue lists each course with its outcomes, labs, hours and level.