|Barak Oshri||Stanford University|
|Annie Hu||Stanford University|
|Peter Adelson||Stanford University|
|Xiao Chen||Stanford University|
|Pascaline Dupas||Stanford University|
|Jeremy Weinstein||Stanford University|
|Marshall Burke||Stanford University|
|David Lobell||Stanford University|
|Stefano Ermon||Stanford University|
Monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals.To this end, the authors investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa.
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.