Humanitarian coordinators operating across large or dispersed refugee settlements routinely work blind. Ground surveys are slow, expensive and dangerous; the result is that clinics, water points, latrines and schools are plotted on maps that are months out of date before the ink dries. Service gaps persist not because resources are absent but because no one can see, in near-real time, which parts of a settlement fall outside a catchment radius.
A small constellation of multispectral microsatellites, combined with SAR passes for cloud-persistent coverage, changes that calculus entirely. Optical imagery at 3–5 m resolution resolves individual structures; change-detection algorithms flag new shelters, demolished latrines or relocated health posts within days of the event. Overlaying known facility coordinates against population-density grids derived from the same imagery produces a continuous service-coverage surface — showing, in metres, how far each household sits from the nearest clean water or primary health point.
The operational payoff is concrete: cluster leads can redirect mobile health units, WASH teams can prioritise borehole siting, and protection actors can identify areas with no visible service footprint — which correlate strongly with vulnerability. A sovereign nation hosting a major refugee population gains something further: an independent, unimpeachable evidence base for negotiations with donors, an audit trail for its own accountability commitments, and the ability to act without waiting for a UN agency to commission and release a commercial imagery task.
Frequently asked
What does 'service coverage mapping' actually measure — is it just counting shelters?
No. Service coverage mapping fuses satellite imagery with ground-reported facility data to answer whether specific services — clean water, primary healthcare, sanitation, education, food distribution — are physically accessible to a given population cluster within an acceptable travel distance (typically ≤1 km for water, ≤5 km for health, per UNHCR standards). Shelter counting is one input, not the output. The output is a service-gap layer that planners can act on.
Why can't humanitarian agencies just use Google Maps or OpenStreetMap for this?
OpenStreetMap is crowd-sourced and valuable for road networks, but it lags crisis events by days to weeks and has no systematic update mechanism tied to population movement. Commercial basemaps like Google Maps are rarely updated at the camp-interior level and are governed by terms of service that restrict derivative humanitarian use. Satellite-tasked imagery, by contrast, can be acquired on demand, processed through change-detection algorithms, and delivered as a classified service-gap product within 24–48 hours of tasking.
How does a sovereign constellation differ in practice from buying imagery from Planet or Maxar?
A sovereign constellation means the nation controls tasking priority, data classification, archival rights, and delivery timelines without a commercial supplier's competing obligations to other customers. When a mass-displacement event occurs — often alongside regional instability — a commercial supplier may deprioritise humanitarian tasking in favour of defence contracts or simply lack available passes over the area of interest. A nationally operated 6–12 satellite microsatellite constellation in a sun-synchronous LEO orbit can guarantee daily revisit over nationally designated priority zones regardless of external commercial conditions.
What resolution is actually needed for service coverage mapping?
For camp-level service infrastructure mapping (clinics, latrines, water points), 0.5–3 m resolution is generally sufficient; Planet's 3 m SuperDove imagery has been validated for this purpose by UNHCR and UNOSAT. For individual structure classification in densely packed informal settlements, sub-metre imagery from BlackSky or Maxar improves accuracy materially. A sovereign constellation designed around 3 m GSD keeps launch mass and per-satellite cost manageable while meeting the core operational requirement.
How does this interact with data protection rules — are displaced people identifiable from these maps?
At 0.5–3 m resolution, individual faces are not discernible, and the output product is an aggregate service-coverage layer, not individual tracking. However, combining coverage maps with mobile network signalling data or biometric registration data could enable re-identification of individuals. Nations operating sovereign systems should apply the UNHCR Data Protection Guidelines (2021) and the ICRC Handbook on Data Protection in Humanitarian Action as binding operational policy, not optional guidance.
Can SAR satellites replace optical ones for this application?
SAR is cloud-penetrating and works day or night, making it indispensable in equatorial or monsoon contexts. However, SAR backscatter is harder to interpret for non-specialist service-coverage attribution — distinguishing a health clinic from a market stall requires more complex ML classification than with optical imagery. The best practice is to use SAR (ICEYE, Capella) for change detection and temporal continuity, then cue optical passes for classification when cloud windows permit.
What role does connectivity play — isn't this a pure EO problem?
EO handles the spatial footprint of services. But whether a telemedicine node, a school, or a health post is actually online and functional requires connectivity monitoring — typically via satellite AIS-style signal detection or LEO IoT pings from providers like Spire Global or Kepler Communications. A complete service coverage picture integrates EO-derived infrastructure maps with connectivity status layers, which is why Camp Communications Provision and Telemedicine are closely linked applications on this platform.
How do nations fund this capability without waiting for a crisis budget?
The World Bank's GEMS (Geo-Enabling Initiative for Monitoring and Supervision) programme and the OECD Development Assistance Committee frameworks both classify satellite-derived humanitarian geospatial infrastructure as eligible Official Development Assistance expenditure. Several middle-income nations have co-funded small EO constellations through blended finance structures — combining domestic budget, World Bank loans, and bilateral technology-transfer agreements — making the business case viable outside emergency supplemental budgets.