Infrastructure quality assessment in Africa using satellite imagery and deep learning

Barak Oshri, Xiao Chen, Marshall Burke, Annie Hu, Pascaline Dupas, David Lobell, Peter Adelson, Jeremy Weinstein, Stefano Ermon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages616-625
Number of pages10
ISBN (Print)9781450355520
DOIs
StatePublished - Jul 19 2018
Externally publishedYes
Event24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom
Duration: Aug 19 2018Aug 23 2018

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/TerritoryUnited Kingdom
CityLondon
Period8/19/188/23/18

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Computational sustainability
  • Deep learning
  • Remote sensing

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