Contaminated Soil Detection: A Proposal Using Machine Learning and Hyperspectral Imaging

Crude oil leakages can produce damages to the environment and the nearby population. Oil companies perform continuous soil and vegetation surveillance around susceptible areas, aiming to identify potential leakages and mitigate their impacts. Traditional methods for monitoring oil leaks are based on visual inspection and losses on the pipeline’s flow-rate signal. These methods allow identifying significant leaks, but small and progressive leaks could pass unperceived for a long time. Therefore, aid monitoring methods that allow more agile soil contamination identification becomes a need. Hyperspectral imaging can offer that agility in the monitoring when employed with library references of contaminated soil. In this study, we propose an intelligent system to identify oil contamination and contamination level, using hyperspectral data as an input for machine learning models. Three different system architectures are proposed and evaluated, aiming to improve identification. The results show the accuracy of 98% in the classification of soils, 94% in oil type, and 93% in contamination level. These observations indicate that this technology has great potential for environmental monitoring of bare soils along pipelines and refineries.