What satellite data types are actually used for wealth mapping?

The main inputs are multispectral optical imagery (for rooftop material, land use, vegetation), nighttime lights (VIIRS from NOAA/NASA), SAR (for structure density in cloudy regions), and ancillary layers such as road networks from OpenStreetMap. Recent studies also fuse daytime and nighttime imagery with mobile call detail records where available. The combination typically outperforms any single source.

How accurate is satellite-derived wealth mapping compared with household surveys?

Peer-reviewed benchmarks report R² values of 0.60–0.80 when predicting survey-based asset indices at village or enumeration-area level. Accuracy drops sharply at individual household level. Governments should treat satellite-derived indices as a screening and prioritisation tool — not a replacement for survey-based benefit eligibility determination.

Why should a government build and operate its own satellite capability rather than buy imagery from Planet or Maxar?

Commercial providers can restrict tasking or raise prices without notice, and many licence agreements prohibit sharing derived data with third-party ministries. A sovereign constellation means the government sets the revisit schedule, owns the raw archive, and is not exposed to foreign export-control regimes. Over a 10-year horizon, owning a microsatellite constellation is often cost-competitive with repeated commercial subscriptions once ground-segment and analysis capacity are internalised.

What resolution do satellites need to be useful for poverty mapping?

Studies show 2–10 m multispectral imagery is sufficient for neighbourhood- and village-level wealth indices. Sub-metre imagery adds value for urban informal settlements where rooftop material and plot size matter. Nighttime light composites at 500 m (VIIRS) remain useful at regional scale. A sovereign programme can tier its architecture: medium-resolution optical for national coverage and targeted VHR for hotspot areas.

Can this work in countries without household survey data?

Transfer learning techniques allow models trained in data-rich countries to be applied in data-poor settings with some accuracy loss — typically 10–20 percentage points lower R². The World Bank's DECAT (Data for Economic Competitiveness and Agility Tool) and the MOSAIKS framework from UC Berkeley are examples of approaches designed for low-data environments. Even imperfect estimates are often more current and spatially granular than no data at all.

What is nighttime light intensity and why does it matter?

NOAA-NASA VIIRS records the brightness of artificial lighting at night at 500 m resolution globally. Brighter areas correlate strongly with economic activity and household electrification. It is one of the most consistently available free global datasets and acts as an important baseline layer in nearly all satellite poverty-mapping pipelines.

How does this application connect to social cash transfer programmes?

Satellite wealth maps can identify unserved or under-targeted geographic areas, informing where registrars should conduct community-based registration. They are most valuable as a geographic filter — narrowing the area in which intensive (and expensive) beneficiary verification is needed. This complements, rather than replaces, national social registries.

What are the main data governance issues a government needs to resolve before deployment?

Key issues include: defining what spatial resolution triggers individual identification under national privacy law; establishing which ministries can access derived outputs; setting data-retention and archival policy for raw imagery; and deciding whether commercial partners can access anonymised training data. OECD's AI Principles and the UN Secretary-General's Roadmap for Digital Cooperation both provide frameworks governments can adapt.

13.6.1Live

Asset-Based Wealth Mapping

Field worker with clipboard interviewing a woman outside a rural mud-brick home surrounded by livestock — Aerial patterns of fields and village rooftops let satellites quietly decode economic life below.

Satellite Imagery Reveals Hidden Household Wealth

1
Satellite Sensor
Captures rooftops, vehicles, crop cover below
2
Image Archive
High-res tiles stored in cloud repository
3
ML Wealth Index
CNN scores assets per household footprint
4
Policy Dashboard
Poverty maps guide cash-transfer targeting

Satellite imagery feeds machine-learning models that score household asset wealth, enabling governments to target social protection accurately.

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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13.6.2 · Next

Welfare Targeting Indicators

Bring this into your sovereign programme.

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