# Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning

arXiv: https://arxiv.org/abs/1711.03654 · HTML: https://mlsanity.com/paper/1711.03654/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 9 of 9)

- [Large Language Models are Geographically Biased](https://mlsanity.com/paper/2402.02680/) (2024)
- [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://mlsanity.com/paper/2310.06213/) (2023)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [Predicting Poverty Level from Satellite Imagery using Deep Neural Networks](https://mlsanity.com/paper/2112.00011/) (2021)
- [Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain](https://mlsanity.com/paper/2203.01068/) (2022)
- [Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach](https://mlsanity.com/paper/2411.02855/) (2024)
- [Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions](https://mlsanity.com/paper/2211.01406/) (2022)
- [A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa](https://mlsanity.com/paper/2212.12084/) (2022)
- [OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data](https://mlsanity.com/paper/2506.03224/) (2025)

## Abstract (arXiv)

Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to acquire, often not updated frequently, and is mainly available for recent years. We train CNN models on free and publicly available multispectral daytime satellite images of the African continent from the Landsat 7 satellite, which has collected imagery with global coverage for almost two decades. We show that despite these images' lower resolution, we can achieve accuracies that exceed previous benchmarks.