Abandoned mining areas are difficult to farm, often unsafe to build on, and expensive to restore. They also pose long‑term environmental risks due to land degradation, water contamination, and unstable ground. However, these hard‑to‑use landscapes are often large, open, and exposed to sunlight, making them promising locations for solar power generation.

In this work, the researchers developed a novel tool called SolarMiner to locate abandoned mining areas and calculate how much solar power they could produce. The tool combines satellite imagery with a computer vision model, a form of artificial intelligence, to identify and classify different types of mining sites. SolarMiner can detect mining areas, determine their type (e.g., open‑pit, subsidence zones, water‑filled pits), measure their surface area, and estimate how much solar capacity could be installed, along with the electricity output and cost.

They tested SolarMiner on a major coal‑mining region in China, Shanxi Province. Focusing only on abandoned mining areas, the model estimated that land‑based solar panels alone could generate over five times the province’s 2023 electricity consumption. Floating solar panels on water‑filled mine pits could generate even more, over six times Shanxi’s usage in the same year.

This research highlights the enormous potential of abandoned mining sites for clean‑energy generation. Using SolarMiner will help direct governments toward smarter planning of solar farms and transmission networks by identifying exactly which mining areas can be transformed into low‑cost, high‑impact renewable‑energy assets.

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A review on modelling methods, tools and service of integrated energy systems in China Nianyuan Wu et al. (2023)