# Seeing SDG 6 from space: local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning Source: [Seeing SDG 6 from space: local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning]() Collection index: [llms.txt]() Data updated: 2026-08-12T05:00:21.403Z Authors: Othmane Echchabi, Aya Lahlou, Nizar Talty, Josh Malcolm Manto, Tongshu Zheng, Ka Leung Lam Published: 2024-11-28T12:13:46.000Z ## Abstract 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 ($R^2 = 0.92$) and show meaningful agreement with safely managed sanitation for sewage ($R^2 = 0.72$). 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Piped water access estimation on African countries without Afrobarometer survey data (external val)](): Piped water access estimation; African countries without Afrobarometer survey data (external val); Weighted MAE: 9.5. - [Sanitation access estimation on African countries without Afrobarometer survey data (external-val)](): Sanitation access estimation; African countries without Afrobarometer survey data (external val); Weighted MAE: 10.7. ## Original sources and implementation [Original paper]() [PDF]()