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In Development · Regional Reconnaissance

OmniMiner

Regional mineral reconnaissance from satellite imagery — spectral analysis for working geologists.

The Problem

Regional reconnaissance over thousands of square kilometres should not require a remote-sensing PhD.

Mineral reconnaissance over greenfield blocks usually starts with weeks of pre-processing Landsat, Sentinel and ASTER imagery — atmospheric correction, band-ratio computation, mineral indices, classification. A solo geologist with a QGIS license cannot keep pace. OmniMiner closes that gap with a user-friendly interface to the same techniques.

What we built

How OmniMiner solves it

  • Spectral analysis pipelines for Landsat 8/9 and Sentinel-2 imagery built directly into QGIS
  • Mineral identification via established band-ratio and PCA techniques — no Python notebook required
  • Designed for greenfield reconnaissance — entire blocks at a time, not single AOIs
  • Output as QGIS-loaded raster + classified vector layers, ready for further interpretation

The outcome

A regional reconnaissance map in days, not weeks. Built for the field geologist who needs spectral analysis without becoming a remote-sensing specialist.

Inputs

Landsat 8/9 imagery · Sentinel-2 imagery · Optional ASTER · Block-scale AoI polygon

Outputs

Mineral classification rasters · Band-ratio composites · QGIS-loaded layers with symbology

Deployment

QGIS plugin · marked "Under Development" in repo

Version In development

The Landing Page

See it for yourself.

OmniMiner landing page

Free Consultation

Curious if OmniMiner fits the work you do for your clients?

Book a 30-minute discovery call with Dr. Amit Tripathi. We will look at what you are delivering today, where these tools could give you leverage, and how a partnership could work — at no cost.

No commitments · White-label terms available · 48-hour reply