TerraEye delivers 51 exploration targets across 100,000 km² for BMRC and IRH

Sep. 3, 2026
By AI, Created 07:00 UTC, Sep 03, 2026, AGP -

TerraEye completed a satellite exploration program for BMRC International Geological LLC and International Resources Holding across nearly 100,000 square kilometers in Africa and South America. The AI-driven screening produced 51 ranked targets, including 15 in dense forest where conventional remote sensing is limited.

Why it matters: - TerraEye's program shows how AI-assisted satellite analysis can narrow vast exploration areas into ranked drill and field targets. - The approach is designed to cut early-stage cost, time and environmental footprint by focusing work on evidence-backed locations. - The forested-area result is notable because conventional spectral methods are largely ineffective under dense canopy.

What happened: - TerraEye completed a multi-region satellite exploration program for BMRC International Geological LLC / International Resources Holding. - The program covered three areas of interest with a combined footprint of about 97,300 square kilometers. - TerraEye delivered 51 ranked exploration targets for field follow-up. - The work spanned two continents and three sharply different terrains. - The announcement was made from Wrocław, Poland, on September 3, 2026.

The details: - One area was an equatorial region of more than 53,000 square kilometers under near-continuous dense forest cover. - A second area was a 38,820-square-kilometer semi-arid, high-altitude magmatic belt prospective for porphyry copper systems. - A third area was a 5,487-square-kilometer arid magmatic arc with strong bedrock exposure in an established copper province. - In the exposed terrains, TerraEye mapped hydrothermal alteration systems from hyperspectral and multispectral satellite data, including EMIT, EnMAP, ASTER and Sentinel-2. - That work produced 26 target zones in the magmatic belt, including eight high-priority targets. - The arid magmatic arc produced 10 priority locations. - In the forested area, TerraEye used Metal-Stressed Vegetation analysis to detect geochemically induced stress in the canopy as an indirect sign of mineralization at depth. - The forested-area work yielded 15 targets cross-validated against geochemical, geophysical and geological datasets. - TerraEye delivered each target with supporting evidence, priority rankings and recommended follow-up work. - Recommended next steps include ground-truthing, geochemical sampling and detailed geological mapping. - Specific target locations were not disclosed for commercial and confidentiality reasons.

Between the lines: - TerraEye's method shows a shift from broad reconnaissance to faster, evidence-led screening of large license areas. - The most distinctive part of the program is the ability to generate targets in dense forest, where direct spectral mapping is usually limited. - BMRC framed the program as a time-saving tool that can complement traditional exploration methods rather than replace them. - The result also reinforces a wider industry trend toward using remote sensing and AI to direct capital toward fewer, higher-confidence targets.

What's next: - TerraEye and BMRC will move the ranked targets into ground follow-up work. - Field teams are expected to test the targets with sampling and mapping before any further exploration decisions. - BMRC said it will continue using the technology platform alongside traditional methods to accelerate its exploration effort. - TerraEye said the program's output is intended to support future exploration for economic metals and minerals worldwide.

The bottom line: - TerraEye turned nearly 100,000 square kilometers of satellite screening into 51 ranked exploration targets, including 15 in dense forest that conventional remote sensing struggles to penetrate. - More information is available in TerraEye's announcement, BMRC's website and IRH's website. - TerraEye's social profile is available on LinkedIn.

Disclaimer: This article was produced by AGP Wire with the assistance of artificial intelligence based on original source content and has been refined to improve clarity, structure, and readability. This content is provided on an “as is” basis. While care has been taken in its preparation, it may contain inaccuracies or omissions, and readers should consult the original source and independently verify key information where appropriate. This content is for informational purposes only and does not constitute legal, financial, investment, or other professional advice.

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