Artificial Intelligence and Satellite Remote Sensing for Mineral Exploration and Mining Site Detection: A Critical Research Review and Reproducible Framework for Gold, Rare Earth Elements, and Critical Minerals

Artificial intelligence (AI) and Earth observation are changing how investigators recognize surface evidence of mineral systems and monitor the footprint of mining activity. Their value is frequently overstated when a remotely sensed alteration anomaly is treated as a discovered orebody or when a site-detection accuracy is transferred uncritically to mineral prospectivity. This research distinguishes three different inferential tasks: mapping surface minerals and alteration, ranking the likelihood of economically relevant subsurface mineralization, and identifying surface disturbance associated with mining. It synthesizes peer-reviewed case studies and mission documentation through October 2026, with particular attention to gold, rare earth elements (REEs), lithium-bearing pegmatites, and artisanal and small-scale mining. Multispectral imagery provides inexpensive regional context, imaging spectroscopy can identify certain diagnostic exposed minerals, radar and elevation data contribute complementary structural and operational evidence, and geological, geochemical, and geophysical data remain essential for defensible prospectivity assessment. Published examples include EnMAP detection of exposed neodymium features at Mountain Pass; deep-forest gold prospectivity modeling in Sudan; convolutional mining-disturbance mapping in Ghana; and a modest-data gold-classification experiment based on pretrained multispectral representations. Their headline accuracy figures are not directly comparable because the target, sample design, spatial scale, prevalence, and reference labels differ. A proposed research protocol is developed around deposit-specific hypotheses, sensor-aware preprocessing, positive-unlabeled and spatially blocked learning, independent field validation, uncertainty-calibrated ranking, and decision-cost evaluation. An Arabian Shield implementation pathway connects these methods to Saudi geological mapping and national data assets without implying that any new deposits have been found. The central conclusion is that satellite-AI is best understood as a transparent, testable system for reducing exploration search space and monitoring disturbance, not as a substitute for subsurface investigation, mineral resource estimation, or lawful ground access. Research type: Critical literature review, methodological synthesis, and prospective research protocol. No new experiments or mineral discoveries are reported. Author: Mohammad Bahareth. MBSR. Evidence cutoff: 26 October 2026. AI assistance disclosure: AI tools assisted in manuscript preparation and subsequent reference checking and correction. This preprint has not undergone independent peer review or experimental replication. Correction note for this version: The revised PDF qualifies the Gallwey study error-rate statement and corresponding table entry, corrects the reference-numbering description, corrects reference 26 first-author attribution to Köhler, and removes the unsupported 2026 date and August 2026 update wording from reference 28. These are bibliographic and wording corrections; no new experiments or results are added. The original version remains in the version history.