# Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning

arXiv: https://arxiv.org/abs/1806.00894 · HTML: https://mlsanity.com/paper/1806.00894/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives](https://mlsanity.com/paper/2102.07004/) (2021)
- [Transfer Learning with Pretrained Remote Sensing Transformers](https://mlsanity.com/paper/2209.14969/) (2022)
- [Classifying Bicycle Infrastructure Using On-Bike Street-Level Images](https://mlsanity.com/paper/2410.19194/) (2024)
- [CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study](https://mlsanity.com/paper/2105.11844/) (2021)
- [AI for Anticipatory Action: Moving Beyond Climate Forecasting](https://mlsanity.com/paper/2307.15727/) (2023)
- [Seeing SDG 6 from space: local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning](https://mlsanity.com/paper/2411.19093/) (2024)

## Abstract (arXiv)

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.