# High-Resolution Poverty Maps in Sub-Saharan Africa

arXiv: https://arxiv.org/abs/2009.00544 · HTML: https://mlsanity.com/paper/2009.00544/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain](https://mlsanity.com/paper/2203.01068/) (2022)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)
- [Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks](https://mlsanity.com/paper/2312.00416/) (2023)
- [Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research](https://mlsanity.com/paper/2501.14546/) (2025)
- [A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models](https://mlsanity.com/paper/2408.01631/) (2024)
- [Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil](https://mlsanity.com/paper/2608.07871/) (2026)

## Abstract (arXiv)

Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.