Stacked encoding and AutoML-based identification of lead-zinc small open pit active mines around Rampura Agucha in Rajasthan state, India.

Accurately discerning lead-zinc open pit mining areas using traditional remote sensing methods is challenging due to spectral signature class mixing. However, machine learning (ML) algorithms have been implemented to classify satellite images, achieving better accuracy in discriminating complex landcover features. This study aims to characterise various ML models for detecting and classifying lead-zinc open pit mining areas amidst surrounding landcover features based on Sentinel 2 image analysis.