[2411.19093] Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning

Computer Science > Computer Vision and Pattern Recognition arXiv:2411.19093 (cs) [Submitted on 28 Nov 2024 (v1), last revised 22 Mar 2026 (this version, v3)] Title:Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning Authors:Othmane Echchabi, Aya Lahlou, Nizar Talty, Josh Malcolm Manto, Tongshu Zheng, Ka Leung Lam View a PDF of the paper titled Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning, by Othmane Echchabi and 5 other authors View PDF HTML (experimental) Abstract:Clean water and sanitation are essential for health, well-being, and sustainable development, yet significant global disparities persist. Although the United Nations' Sustainable Development Goal (SDG) 6 clearly defines targets for universal access to clean water and sanitation, limitations in data coverage and openness impede accurate tracking of progress in many countries. To bridge these gaps, this study integrates Afrobarometer survey data, satellite imagery from Sentinel-2, and advanced deep learning techniques using Meta's self-supervised Distillation with No Labels (DINO) model to develop a modeling framework for evaluating access to piped water and sewage systems across diverse African regions. The modeling framework achieved notable accuracy, with over 84% for piped water and 87% for sewage system access...