Department Systems Analysis, Integrated Assessment and Modelling

While essential for mineral production and the energy transition, mining operations and their associated tailings storage facilities are among the least systematically monitored sources of water contamination worldwide. These assets are spatially dispersed, often remote, and evolve rapidly over time, making comprehensive inventories and direct water-quality monitoring difficult to maintain. As a result, the location, extent, and chemical risk profiles of many mining-related water bodies remain poorly characterized. HyperTails seeks to address this gap by combining high-resolution satellite imagery to identify mining assets at scale with hyperspectral remote sensing and machine learning to characterize chemically contaminated tailings. By integrating asset detection, spectral analysis, and contaminant information extracted from mining literature, the project aims to enable global, scalable screening of mining impacts on water quality, particularly in regions where regulatory oversight and in-situ data are limited.