Estimating Poverty Using Deep Learning Algorithms

While there is an existing model created by the World Bank team to estimate poverty levels based on existing survey data, students were tasked to improve the accuracy rate of this model. This initial model only utilized three spectral bands to capture data through satellite imagery and had an original gridsearch model. Students expanded various hyperparameters and created a newer model to help achieve higher accuracy as well as create a continuous measure of poverty. By the end of the challenge, students were able to increase their poverty prediction accuracy rates by 3.8%. One way in which students improved upon the original model was by increasing the three spectral bands to seven, with each band capturing light from different parts of the electromagnetic spectrum. This was beneficial as the various combinations of bands are useful at measuring various poverty indicators. Students also explored changing various parameters to improve model performance, including altering batch sizes and adding more hidden dense layers. Furthermore, by expanding the initial gridsearch to handle more continuous outputs rather than just providing a binary outcome allows for a more comprehensive understanding of poverty levels.