Identifying impervious surfaces for rainwater harvesting feasibility using unmanned aerial vehicle imagery and machine learning classification
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Abstract
As global demand for clean, reliable water sources continues to increase amid a growing population and the impacts of climate change, effective water conservation and collection methods are more important than ever. Rainwater harvesting has long been a reliable technique for capturing and preserving water, and recent advances in geographic information systems (GIS), unmanned aerial vehicle (UAV) remote sensing, and machine learning (ML) image classification have significantly improved our ability to accurately assess the potential for rainwater harvesting. This study leveraged these technologies to evaluate the feasibility of rainwater harvesting at the Osmanbey Campus of Harran University, using UAV imagery and ML classification techniques to identify suitable surfaces for collection. The results showed that it is possible to irrigate a grass area of 4417 square meters daily for a year using the potential harvested rainwater in the study area, demonstrating the significant potential of rainwater harvesting as a sustainable water source for irrigation purposes.
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