Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
This provides a low-cost, scalable solution for poverty mapping in developing countries, though it is incremental as it builds on prior work using satellite imagery.
The researchers tackled the problem of predicting local economic livelihoods in developing countries by training CNN models on free, publicly available Landsat 7 satellite imagery, achieving accuracies that exceed previous benchmarks.
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.