Abstract

Mine Waste Storage Facilities (MWSFs) in Chile present substantial environmental and safety risks due to their extensive scale and the hazardous nature of their contents. This study proposes an automated detection approach that integrates Sentinel-2 satellite imagery with advanced deep learning models to address these critical issues. A central contribution of this research is the development of MineWasteCL_DB, a comprehensive public dataset comprising over 30,000 annotated images and 320,093 labels for diverse MWSF types, including Tailings Storage Facilities (TSFs), Waste Rock Dumps (WRDs), and Leaching Waste Dumps (LWDs). The study employs the YOLOv8x-seg model, selected for its high precision, to validate the presence of 96.15% of officially registered TSFs. Furthermore, it identified 141 WRDs and 112 LWDs in the Antofagasta Region, facilities absent from any official national registry. These findings underscore the methodology’s potential for widespread application and the necessity for routine monitoring across additional regions. The results provide a robust framework for advancing the understanding and management of MWSFs, thereby improving regulatory oversight and promoting environmental safety. The methodology supports not only the efficient monitoring of registered facilities but also the preliminary identification and prospective registration of unregistered sites. This capability enhances the oversight capacities of regulatory authorities while fostering the protection of environmental and public safety