Poverty Estimation with Satellite Imagery at Neighborhood Levels - Results and Lessons for Financial Inclusion from Ghana and Uganda

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Title
Poverty Estimation with Satellite Imagery at Neighborhood Levels - Results and Lessons for Financial Inclusion from Ghana and Uganda
Author
Soren Heitmann and Sinja Buri
Year
2019
Abstract
This document summarizes a study on leveraging digital tools like satellite imagery, mobile data, and machine learning to improve poverty estimation and financial inclusion efforts in Sub-Saharan Africa. It highlights innovative methods, such as spatial boosting, to generate precise, neighborhood-level poverty predictions, validated by ground-truth surveys in Ghana and Uganda.
Resource Subject
  • Poverty
Keywords/Access Points
Machine Learning; Satellite Imagery; Financial Transaction Patterns
Geographic Locations
  • Ghana
  • Uganda
Language
  • English
Special Collections
  • Knowledge Exchange Resources
Archival Record ID
MFA.2024.0824