Abstract

In the global mining industry, periodic monitoring of large Mining Waste Deposits (MWDs) is essential. This paper presents an innovative approach leveraging advanced artificial intelligence techniques combined with high-resolution Sentinel satellite imagery to accurately geolocate MWDs in Chile. Our methodology involves segmenting satellite images, expert labeling, and training state-of-the-art models for precise detection. Experimental results, as summarized in the table below, highlight the superior performance of the YOLOv7 model with an AP Box of 0.75 and an AP Mask of 0.72, outperforming other tested configurations like Mask RCNN with FPN 3X. The results demonstrate the potential of this approach in optimizing the management and monitoring of MWDs, contributing significantly to safety and sustainability in the mining sector. This research not only provides insights into the capabilities of AI in geospatial analysis but also sets a benchmark for future studies in the realm of mining and environmental management.