Why should a government own catastrophe-modelling satellites rather than licensing data from Planet, ICEYE or Maxar?

Commercial providers can suspend, reprice or restrict data access at contract renewal — or under the export-control regimes of their home jurisdictions. A sovereign constellation guarantees uninterrupted access to calibrated imagery for actuarial and regulatory purposes regardless of geopolitical conditions. It also allows the government to monetise derived products domestically and retain the intellectual property in the exposure database.

What satellite modalities are most useful for catastrophe modelling inputs?

SAR (C- or X-band) is the workhorse for flood extent, structural damage detection and soil-moisture estimation because it penetrates cloud and operates day/night. Multispectral optical at 1–3 m resolution maps building footprints and crop areas for exposure databases. Thermal infrared adds wildfire heat-source detection. A complete sovereign program ideally hosts two or more of these modalities across a shared constellation.

How does satellite data actually feed into a catastrophe model?

Cat models have three components: a hazard module, an exposure database and a vulnerability function. Satellites contribute to all three: SAR and radar altimetry sharpen flood and storm-surge hazard footprints; high-resolution optical imagery builds and updates the exposure database (building type, age, density); and post-event damage imagery calibrates vulnerability functions so future loss estimates are regionally accurate rather than transplanted from North American or European loss histories.

Is a nanosatellite constellation accurate enough for actuarial use?

For hazard-footprint delineation and macro-scale exposure mapping, 3–5 m resolution microsatellites are generally sufficient and are already used operationally by EUMETSAT and ESA's Copernicus program. For detailed building-level damage grading required to settle individual property claims, 0.3–0.5 m resolution from a larger microsatellite or a hosted payload is preferable. A phased architecture — starting with a 6-unit microsatellite constellation and adding a higher-resolution element in Phase 2 — balances cost against actuarial precision.

What is the protection gap and how does satellite data help close it?

The protection gap is the share of economic losses from disasters that is not covered by insurance — globally around 62 % according to Swiss Re. It is largest in lower-income countries where exposure databases are weakest and insurers lack confidence to price risk affordably. Satellite-derived exposure and hazard data reduces pricing uncertainty, enabling insurers to lower loadings and extend coverage to underserved populations. The World Bank's disaster risk financing programs explicitly cite improved earth-observation data as a lever for gap reduction.

Can sovereign satellite data support parametric insurance and catastrophe bonds?

Yes, and this is one of the strongest financial cases for a sovereign constellation. Parametric products pay out when a measurable index — flood depth, wind speed, shaking intensity — crosses a threshold, rather than requiring loss adjustment. Satellite-derived indices (e.g., SAR-measured inundation area) can serve as objective, near-real-time triggers that are verifiable by all parties. The sovereign operator becomes the trusted, independent data source, reducing basis risk disputes and accelerating payouts.

How long does it take to build a basic sovereign cat-modelling constellation?

A 4–6 unit microsatellite constellation based on established bus platforms (e.g., SSTL-150, GomSpace, Satellogic) can be commissioned and launched within 24–36 months of program start with an experienced prime integrator. Ground segment, data pipeline and analytical platform development runs in parallel and is often the longer pole. Procuring a hosted payload on an existing government satellite can shorten first-light to 12–18 months if coverage breadth requirements are modest.

Which international organisations produce standards or benchmarks relevant to this application?

ISO/TC 211 governs geospatial metadata (ISO 19115) essential for data-sharing with insurers and reinsurers. The OGC defines interoperability standards for Earth-observation web services. The WMO coordinates hazard observation standards used to cross-validate satellite-derived weather perils. IAEA provides guidelines for satellite-assisted damage assessment in post-nuclear or industrial-accident scenarios. ITU-R allocates the radio-frequency spectrum satellite SAR and passive sensors depend on, making ITU coordination a prerequisite for any sovereign constellation.

12.1.1Live

Catastrophe Modelling Inputs

Insurance worker in a waterlogged street recording damage to homes after a major flood — Cars navigating flooded streets generate real-world loss data that satellite-fed catastrophe risk models use to sharpen future disaster forecasts.

Satellite Data Feeding Catastrophe Risk Models

1
Earth Observation
SAR and optical sensors capture hazard extent
2
Downlink & Ingest
Raw imagery downlinked to ground stations hourly
3
Hazard Analytics
Flood, wind, quake footprints modelled against exposure
4
Insurer Platform
Cat model updates loss estimates for portfolios

Satellite imagery feeds near-real-time hazard footprints into catastrophe models, enabling insurers to rapidly quantify and price disaster losses.

Page in the build queue

This application has its place in the atlas. The full page — short and long descriptions, what-matters bullets, sovereignty rationale, and the eight-field architecture block — is being written next.

Maturity reading: Operational systems exist with paying customers. Phase 1 built §4.1 (Maritime Intelligence) end-to-end as the depth-and-format reference. Section 4 is the next section to graduate from scaffold to full content; the rest follow on the roadmap.

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12.1.2 · Next

Pre-Loss Risk Assessment

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