Low-Cost Geological Reconnaissance for Artisanal and Small-Scale Mining: RGB–HSV Analysis of Rendered Google Earth Imagery in Arid Copper-Prospective Terrains of Chile and Balochistan

Advanced multispectral and hyperspectral remote sensing supports mineral and alteration mapping, but its imagery, specialist expertise and validation requirements may remain inaccessible to artisanal and small-scale mining operators during first-pass evaluation. This study evaluates how much defensible geological reconnaissance information can be extracted from rendered Google Earth imagery without interpreting display color as mineralogical evidence. Google Earth Pro RGB exports displaying Airbus Pléiades imagery were processed using a reproducible workflow combining preprocessing, RGB–HSV transformation, color-class delineation, spatial-pattern assessment and lineament analysis. Image-derived classes were treated as color-defined surface indicators and evaluated against documented geological and structural evidence. Two Chilean IOCG-related reference cases represented contrasting geometries: Farellon displayed narrow, structurally aligned patterns, whereas El Morado displayed a broader, discontinuous corridor-scale distribution. Two underexplored Chilean targets displayed corresponding linear and patchy patterns. A documented skarn occurrence served as a contrasting mineral-system case. Kabul Koh and Ziarat Malik Karkam in Balochistan’s porphyry Cu–Au-prospective Chagai magmatic belt were used to test cross-regional and cross-mineral-system applicability. Across the cases, the workflow delineated contrasting surface-pattern geometries and their spatial relationships with interpreted structures without inferring mineral identity or deposit type from the RGB–HSV classes alone. The method therefore provides a low-cost, constraint-conditioned reconnaissance and target-prioritization procedure for arid copper-prospective terrains, supporting preliminary reassessment of existing or abandoned artisanal workings.