A Deep Learning Approach to Water Point Detection and Mapping Using Street-Level Imagery in Lagos, Nigeria
Affiliation: MIT Senseable City Lab
Completion: July 2024
Citation: Neil Patel. "A Deep Learning Approach to Water Point Detection and Mapping Using Street-Level Imagery." Water Practice and Technology 19, no. 9 (September 1, 2024): 3485–3494. https://doi.org/10.2166/wpt.2024.197.
Households in developing countries often rely on alternative shared water sources that exist outside of the datasets of public service providers. This poses a significant challenge to accurately measuring the number of households outside the public service system that use a safe and accessible water source. This paper proposes a novel deep learning approach that utilizes a convolutional neural network to detect water points in street-level imagery from Google Street View. Using a case study of the Agege local government area in Lagos, Nigeria, the model detected 36 previously unregistered water points across a variety of urban settings and obstruction levels with 94.7% precision. The paper was developed through the support of Prof. Carlo Ratti and the MIT Senseable City Lab.
I supported the Philippines' national water agency (DENR-WRMO) to adapt and utilize this methodology to identify barangays without Level III systems for a modular desalination pilot.
More
- [Data] Updated Detections (2/27/25) Link
- [Data] Roboflow Model and Training Dataset Link
- [Code] GitHub Repository Link
- [Poster] 2023 UNC Water & Health Conference Link
- [Slides] Technology Showcase Presentation at UNC Water & Health Conference Link
- [Publication] Journal of Water Practice and Technology Link