Chile's mining industry, a global leader in copper production, faces challenges due to increasing volumes of mining waste, particularly Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD). The National Service of Geology and Mining (SERNAGEOMIN) requires assessment of the physical stability (PS) of these facilities, but current methods are hindered by data scarcity and resource constraints. This study proposes a simplified evaluation methodology using first-order parameters from open-access data. By integrating Geographic Information Systems (GIS) and Artificial Intelligence (AI)-utilizing models like YOLOv11 and convolutional neural networks-we automate the detection and characterization of WRD and LWD from satellite imagery, extracting critical parameters for PS assessment. This approach reduces analysis time and minimizes human error. Validated in the Antofagasta Region, Chile's primary mining area, we identified and evaluated 70 WRD and 54 LWD. The results demonstrate the effectiveness of prioritizing deposits based on potential risk, enhancing SERNAGEOMIN's capacity for supervision. The successful application suggests scalability to other mining regions and adaptability to different facility types, including tailings storage facilities. This work offers a practical tool to improve safety and risk management in the mining industry, addressing critical challenges in PS evaluation under current regulatory constraints.

Artificial intelligence, closure plan, geographical information systems, mine waste storage facilities, physical stability assessment, Sentinel-2 satellite imagery, YOLOv11. I, II, III, IV, V, MR, VI, VII, and XI of Chile, reaching the following sizes: i) WRDs vary between 50 and 500 m in height, with projections up to 1,000 m in large mining projects, occupying thousands of hectares (Figure 1.a); ii) LWDs have heights ranging from 10 to 120 m and occupy areas covering hundreds of hectares (Figure 1.b).

To date, these types of MWSF have demonstrated adequate mechanical behavior under static and seismic conditions. However, in Chile, mining companies are legally obligated to assess and ensure their physical stability (PS), even after closure, to safeguard human life and health and to protect the environment, in accordance with national legislation [5], [6]. A potential failure in the PS of these facilities could result in environmental disasters, significant material damage, and even loss of human life, as has been reported in other mining countries [7], [8].

The National Service of Geology and Mining (SER-NAGEOMIN), a government entity responsible for the supervision and oversight of MWSF, faces a growing challenge due to the need to ensure the long-term PS of these mining facilities. To guide companies and inspectors in assessing PS, SERNAGEOMIN issued the ''Methodological Guide for the Evaluation of the Physical Stability of Residual Mining Facilities' ' [9], hereinafter referred to as the PS Guide. This tool standardizes and regulates the procedures for evaluating the PS of MWSF. The evaluation process outlined in the PS Guide requires a range of physical parameters and environmental data, including both the design project and the closure plan for these facilities [5], [10]. Additionally, information from periodic monitoring conducted during their operation and construction phase is essential. Using this data, the PS Guide employs a matrix analysis to assess stability condition for various potential failure mechanisms. However, since its official adoption in 2018, the effective implementation of this evaluation and oversight tool has faced several technical barriers. These include the lack of a national MWSF registry, insufficient number of inspectors, difficulties in obtaining critical geotechnical parameters (e.g. foundation soils for MWSF), and stringent administrative requirements imposed by national regulations.

In this context, to address the current challenges associated with applying the PS Guide, this work proposes a simplified evaluation methodology. This methodology uses first-order parameters to estimate the global PS condition of WRD and LWD, using state-of-the-art technologies such as geographic information systems (GIS) and artificial intelligence (AI). GIS enables the integration and analysis of multiple layers of geospatial data, such as geology, topography, seismicity, and precipitation, complemented by specific geotechnical data on the waste [11], [12]. AI facilitates the automatic detection and geometric characterization of these deposits, reducing analysis times and minimizing human errors. This methodology, including proposed matrices and operational approach, will be validated through a case study in the Antofagasta Region, a key mining area in Chile with major operations