How AI-powered geospatial analytics are revolutionising the mining sector

It is no secret that mining operations face numerous challenges in monitoring vast infrastructure for issues such as monitoring reclamation efforts, unexpected land movements, and acid drainage. Maintaining safety and operational integrity requires proactive threat identification.

So, how does a mining organisation identify potential threats before they escalate? Using advanced AI-powered algorithms to analyse satellite imagery, geospatial analytics can monitor mining assets and infrastructure, providing actionable alerts to problems. Mining byproducts can be hazardous to environmental and population health, but companies often struggle to address certain issues due to limited detection capabilities.

The terrain controlled by mining companies is vast, this makes identifying discrete changes challenging. These changes can create a needle-in-the-haystack approach to problem identification. Often, these ‘small’ problems are identified only after they've spiralled out of control.

This is where AI-powered geospatial analytics has shown significant promise. Equipping mining companies with more detailed insights into their holdings is helping both to protect the industry and to point it toward new sources of value.

Monitoring challenges in the mining sector

The complexities of monitoring generated waste can keep mining executives up at night. Tailings ponds and protective dams require vigilant oversight, and the challenges of the reclamation process are equally steep: dams are continually threatened by vegetation growth, and small instances of land movement or erosion are chronically under-detected.

The leaks and spills that result from inadequate monitoring pose an existential risk to mining concerns. Manual conventional monitoring techniques may serve a purpose, but as recent tailings spills suggest, they might not be enough.

AI-enhanced geospatial analytics are the answer

This is where geospatial analytics comes in. It’s a two-part process; first, the terrain is mapped using advanced satellite imagery. Then, these data-rich reproductions are analysed using cutting-edge AI algorithms, which can quickly identify issues and provide mine owners with concise, actionable insights into their properties.

Monitoring mining activities in a timely manner using satellite or drone imagery allows mining operations to optimise equipment usage, track progress, and identify potential safety or environmental hazards. For tailings pond management, this technology can measure a wide variety of heavy metals on land or in water. It can monitor and measure landslips, landslides, and earth movement around critical dam facilities and to the surrounding area. In instances of mine closure, it can track loss of vegetative cover, helping to protect dams and embankments. Furthermore, geospatial analytics can map soil and water areas down to the square foot, ensuring the rapid detection of acid mine drainage.

Geospatial analytics can also include innovative solutions for the rare earth elements (REE) sector. With geopolitical and supply chain challenges limiting REE accessibility, the technology is poised to help mining organisations identify new opportunities and navigate this critical market.

The benefits here extend beyond compliance. Because AI-enhanced geospatial analytics can detect both the presence of specific materials and their concentration, it can point mine owners toward potential mineral deposits. Given the volatile state of the REE sector, the implications here are significant.

In the mining sector, information is power. AI-enhanced geospatial analytics are granting mining companies that power, and changing the face of mining operations the world over.

Read the article online at: https://www.globalminingreview.com/mining/10022025/how-ai-powered-geospatial-analytics-are-revolutionising-the-mining-sector/