# Seeing SDG 6 from space: local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning

arXiv: https://arxiv.org/abs/2411.19093 · HTML: https://mlsanity.com/paper/2411.19093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning](https://mlsanity.com/paper/1806.00894/) (2018)
- [An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine](https://mlsanity.com/paper/1912.10726/) (2019)
- [Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery](https://mlsanity.com/paper/2210.06786/) (2022)

## Abstract (arXiv)

Access to drinking water and sanitation is essential, yet monitoring progress toward Sustainable Development Goal 6 remains constrained by costly, infrequent, and spatially uneven household surveys, particularly in data-scarce regions. We develop a scalable remote-sensing framework to estimate the area-level presence of piped water and sewage systems across Africa at 2.56 km resolution. The framework combines Sentinel-2 imagery, enumerator-observed system-presence records from Afrobarometer enumeration areas, 30 m population data, and Vision Transformer representations learned through DINO self-supervised learning. On held-out enumeration areas, the best models achieve AUROCs of 91.54% for piped water and 93.24% for sewage. Under leave-one-region-out cross-validation, performance declines to 75.5% and 78.7%, respectively, indicating challenges in transferring models to unsampled regions. Applied across 50 African countries, population-weighted estimates closely track WHO/UNICEF Joint Monitoring Programme benchmarks for piped water access (R2 = 0.92) and show meaningful agreement with safely managed sanitation for sewage (R2 = 0.72). In countries without Afrobarometer coverage, population-weighted mean absolute errors are 9.5% for piped water and 10.7% for sewage. Predictions for 767 Local Government Areas in Nigeria reveal substantial subnational inequality: in the most affected areas, as many as 1.187 million people live where no piped water system is present and 1.577 million where no sewage system is present. These findings show that self-supervised learning with freely available satellite imagery can complement household surveys and support SDG 6 monitoring, infrastructure planning, and environmental equity assessment.