Open data for algorithms: mapping poverty in Belize using open satellite derived features and machine learning

Several methods have been proposed for using satellite imagery to model poverty. These include poverty mapping using convolutional neural networks applied either directly or using transfer learning to high resolution satellite images, or combinations of methods that combine satellite imagery with standard methods. However, these methods require proprietary imagery which, given their cost and infrequent acquisition, may render these advances impractical for most applications. The authors investigate how satellite-derived poverty maps may improve when incorporating features derived from Sentinel-2 and MODIS imagery, which are both open-source and freely and readily available. The authors estimate a poverty map for Belize which incorporates spatial and time series features derived from these sensors, with and without survey derived variables. They document an 8% percent improvement in model performance when including these satellite features and conclude by arguing that Open Data for Development should include open data pipelines where possible.