Water infrastructure and hydrocarbon production systems are increasingly exposed to ageing networks, rapid urbanization, and climate variability, which collectively exacerbate non-revenue water losses and oil spill incidents with severe environmental and socioeconomic consequences. This systematic review synthesizes empirical evidence on the application of remote sensing and Geographic Information Systems (GIS) for monitoring, detecting, and managing water loss and oil spills across diverse hydrological, industrial, and coastal settings. The review examines the performance of satellite, airborne, uncrewed aerial vehicle, and in-situ sensor platforms, in combination with machine learning and advanced image processing, for leak localization, spill delineation, and change detection. It examines the role of GIS-based spatial analytics in integrating hydraulic, infrastructural, environmental, and socioeconomic datasets to support risk mapping, early warning systems, and decision-making support. The findings demonstrate that integrated RS–GIS approaches significantly enhance detection accuracy, spatial coverage, and temporal responsiveness compared to conventional monitoring methods, while also revealing persistent challenges related to multi-sensor data fusion, algorithm transferability, ground validation, and operational continuity under adverse environmental conditions. The review highlights the significant potential of RS–GIS frameworks to enhance evidence-based planning and proactive management of water and oil infrastructure, particularly in data-scarce and resource-constrained contexts.
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Ndivhuwo Ramovha
Makhutsisa Charlotte Mokoatle
Phoka Rathebe
Water Resources Management
University of Johannesburg
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Ramovha et al. (Sun,) studied this question.
www.synapsesocial.com/papers/69ba428e4e9516ffd37a2ef9 — DOI: https://doi.org/10.1007/s11269-026-04508-3