Poverty Analytics — Health, Education, Humanitarian Development & Social Protection — Satellize

13.6Subsection · 5 applications

Poverty Analytics

Community health worker with tablet interviewing a family outside a rural home — Aid workers supporting elderly residents in rural areas benefit from satellite data that pinpoints underserved communities often invisible to traditional surveys.

Satellite Data Revealing Hidden Poverty Patterns

1
Observation
Multispectral satellites capture nightlight, rooftop density
2
Downlink
Raw imagery streamed to ground receiving stations
3
AI Analytics
ML models estimate wealth indices at village scale
4
Policy Tool
Governments target social transfers to poorest households

Satellite imagery feeds machine-learning models that generate granular poverty maps guiding social-protection spending in underserved regions.

Other areas in Health, Education, Humanitarian Development & Social Protection

13.5 · Previous

Education Connectivity

Humanitarian Mapping

Subsection 13.6 — Health, Education, Humanitarian Development & Social Protection

Poverty Analytics

5 applications.

Applications

13.6.1 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.
13.6.2 Welfare Targeting Indicators — Deriving objective, near-real-time proxy indicators of household welfare — nightlight intensity, roof material, crop stress, flood exposure — from multispectral and SAR satellite imagery to guide social protection targeting.
13.6.3 Subnational Poverty Indices — Constructing district- and village-level poverty indices by fusing satellite-derived proxies — nightlight intensity, built-environment density, crop yield, land cover — with ground survey data.
13.6.4 Cash Transfer Programme Targeting — Using multi-source satellite imagery and derived socioeconomic proxies to identify and verify eligible households for national cash transfer programmes at scale.
13.6.5 Service Access Inequality Mapping — Quantifying geographic disparities in access to health, education, water and financial services by fusing satellite-derived settlement and infrastructure data with ground truth.