13.6.1 — Poverty Analytics — maturity: live

Asset-Based Wealth Mapping

Using satellite imagery and machine-learning to infer household and community wealth from observable physical assets — rooftop materials, road access, vehicle density, agricultural holdings.

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National statistics offices in lower- and middle-income countries typically wait five to ten years between household surveys, leaving policymakers blind to where poverty is deepening or retreating between census cycles. Satellite-derived asset proxies — roof type, built-area density, proximity to paved roads, irrigated field extent — correlate strongly with validated survey wealth quintiles and can be produced annually at ward or village level for a fraction of the cost of a ground enumeration.

A constellation of optical microsatellites in sun-synchronous LEO provides the sub-3-metre multispectral imagery needed to resolve individual structures reliably across a country's full territory. Paired with synthetic aperture radar passes for cloud-affected regions and seasons, the stack delivers consistent annual coverage regardless of weather. On-board preprocessing reduces downlink volume; a sovereign GPU cluster runs the computer-vision inference pipeline and produces ward-level asset-index scores that are audit-ready and reproducible.

The operational payoff is a living wealth map that a ministry of finance or planning commission can interrogate monthly rather than decennially. Subnational governments gain objective evidence for budget allocation. International donors can verify that transfers reach the intended quintiles without waiting for the next survey cycle. And because the data are produced by a national system under national protocols, the methodology is open to parliamentary scrutiny — a guarantee no commercial data subscription can match.

What matters

Roof-material classification at 2–3m resolution is the single most validated remote asset proxy across sub-Saharan Africa and South Asia.
Survey-to-satellite wealth index correlations exceed r = 0.85 in peer-reviewed studies when training data cover at least three agro-ecological zones.
Annual satellite revisit cycles let governments detect poverty rebounds after shocks — drought, flood, inflation — before the next household survey.
A sovereign pipeline eliminates the risk that a commercial vendor withdraws access, changes licensing terms, or geo-blocks imagery during a political dispute.

Sovereignty score: 8/10 — A nation that relies on a foreign commercial provider for its poverty map surrenders control over the most politically sensitive domestic dataset a government produces.

Wealth and asset data at ward level are constitutionally sensitive in many jurisdictions; routing them through a foreign cloud pipeline creates legal exposure under data-protection and statistical-secrecy statutes.
Commercial imagery vendors have suspended or re-priced access to politically inconvenient governments on short notice — a sovereign constellation is immune to that leverage.
Donor-driven poverty maps produced by external NGOs or multilaterals have historically driven budget allocations that contradict national planning priorities; a sovereign system gives the government the authoritative dataset.
Reproducibility and parliamentary accountability require that the methodology, training labels and inference code are held domestically and are subject to national audit — conditions no third-party subscription can guarantee.

Reference architecture

Payload: Multispectral optical imager, 4-band (RGB + NIR), 2.5m GSD, 20km swath; secondary panchromatic channel at 0.8m for structure-level classification; optional SAR cross-cue from shared radar constellation at 3m spotlight resolution
Bus class: ESPA-class microsat, 120kg, 600W total power, 400W available to payload; deployable solar panel array; 16 TB solid-state mass memory for store-and-forward
Orbit: Sun-synchronous LEO at 500–550km; 18-satellite walker constellation; 3-day full-national-territory revisit at equator, daily revisit of priority zones; LTAN 10:30 for consistent shadow angle
Ground segment: 3-station national network (X-band downlink, S-band TT&C) co-located with the national meteorological and national mapping agencies; SatNOGS nodes at two universities for telemetry backup; on-premise GPU cluster (≥8× A100-class GPUs) for inference
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References

Predicting poverty and wealth from mobile phone metadata — Science (replication studies cite satellite inputs) — The World Bank's poverty measurement programme documents satellite and big-data methods for inter-survey poverty estimation, including asset-index approaches validated against LSMS survey data.
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa — Nature Communications — Researchers demonstrate that daytime satellite imagery fed to convolutional neural networks predicts asset-wealth index values at village level with high accuracy across seven African countries.
FAO Earth Observation for Agricultural Statistics — FAO's earth-observation programme shows how land-use and crop-area mapping from satellite data feed into national poverty and food-security assessments at subnational scale.
UNDP Human Development Data — Multidimensional Poverty Index methodology — The MPI framework defines asset ownership as a core deprivation dimension; satellite-derived proxies are increasingly cited as a mechanism for updating MPI estimates between survey rounds.
ESA — Earth Observation for Sustainable Development Goals — ESA documents how Copernicus Sentinel data underpin SDG indicator monitoring, including SDG 1 (no poverty) metrics derived from land-cover and built-environment analysis.