  The UN Sustainable Development Goals allude to the importance of
infrastructure quality in three of its seventeen goals. However, monitoring
infrastructure quality in developing regions remains prohibitively expensive
and impedes efforts to measure progress toward these goals. To this end, we
investigate the use of widely available remote sensing data for the prediction
of infrastructure quality in Africa. We train a convolutional neural network to
predict ground truth labels from the Afrobarometer Round 6 survey using Landsat
8 and Sentinel 1 satellite imagery.
  Our best models predict infrastructure quality with AUROC scores of 0.881 on
Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using
Landsat 8. These performances are significantly better than models that
leverage OpenStreetMap or nighttime light intensity on the same tasks. We also
demonstrate that our trained model can accurately make predictions in an unseen
country after fine-tuning on a small sample of images. Furthermore, the model
can be deployed in regions with limited samples to predict infrastructure
outcomes with higher performance than nearest neighbor spatial interpolation.
