A comprehensive exploration of nongeostationary satellite systems in the mining industry: emphasizing AI , ethical considerations, and communication strategies

Non-geostationary satellite (NGSO) constellations—particularly LEO/MEO—are transforming mining by providing low-latency connectivity and taskable Earth observation to remote, infrastructure-poor sites. Objectives include mapping NGSO applications across exploration, planning, and operations; assessing AI's role in tasking, routing, and analytics; and examining governance and ESG implications, with a focus on Africa and East Africa. Methods involved a PRISMA-aligned systematic review (protocol registered) synthesising primary and secondary evidence on NGSO-enabled EO and communications in mining. A random-effects meta-analysis was planned if three or more comparable studies reported the same outcome; otherwise, a structured narrative synthesis with predefined subgroups (LEO vs MEO, EO vs backhaul, open-pit vs underground, Africa vs elsewhere) was used. Results and discussion showed that across more than 30 use cases, NGSO backhaul and EO tasking consistently reduced time to insight for pit progression, tailings surveillance, and asset tracking; simulations indicated routing improvements of approximately 10% on tree topologies and 30% on mesh networks at N=500, demonstrating tangible latency and capacity benefits for safety-critical workflows. Continuity was enhanced through multi-sensor PNT (GNSS/inertial/vision plus radio localisation ) and hierarchical link adaptation that rapidly re- parameterises under noise, weather, or interference. AI added value by improving tasking and congestion control in edge and cloud inference, though it required cascaded models, compression, and uncertainty gating to meet compute and bandwidth constraints. Governance themes—such as data protection, transparency, and community benefit—were recurring enablers of adoption. Conclusion: When combined with resilient positioning, adaptive operations, and credible ESG safeguards, NGSO combined with AI can significantly enhance mining efficiency, safety, and sustainability; priorities include standardised KPIs, transparent cost models, and long-term pilot deployments.