Towards Explaining Satellite Based Poverty Predictions with
  Convolutional Neural Networks
Hamid Sarmadi Thorsteinn Rögnvaldsson Nils Roger Carlsson Mattias Ohlsson Ibrahim Wahab Ola Hall
Deep convolutional neural networks (CNNs) have been shown to predict poverty
and development indicators from satellite images with surprising accuracy. This
paper presents a first attempt at analyzing the CNNs responses in detail and
explaining the basis for the predictions. The CNN model, while trained on
relatively low resolution day- and night-time satellite images, is able to
outperform human subjects who look at high-resolution images in ranking the
Wealth Index categories. Multiple explainability experiments performed on the
model indicate the importance of the sizes of the objects, pixel colors in the
image, and provide a visualization of the importance of different structures in
input images. A visualization is also provided of type images that maximize the
network prediction of Wealth Index, which provides clues on what the CNN
prediction is based on.