Computer Vision in Mining Exploration: Beyond the Marketing Slides

Last month I sat through a pitch deck where a vendor claimed their computer vision model could "detect gold deposits with 94% accuracy from satellite imagery alone."

I almost laughed. Then I got annoyed.

Because this is the problem with how computer vision is being sold to the mining industry right now. Slick demos, confident percentages, and almost nothing that holds up when you walk the ground in Skardu or Chagai.

So let me tell you what actually works, what doesn't, and where I got it wrong myself.

What Computer Vision Actually Does in Exploration

First, the honest version. Computer vision in mining isn't some magical mineral-detector. It's pattern recognition applied to spectral and structural data. That's it.

When we run a Sentinel-2 scene through our models at GeoMine AI, we're not asking the computer to find gold. We're asking it to find the signatures that geologists have correlated with gold-bearing systems for the last 60 years — iron oxide staining, argillic alteration, hydroxyl-bearing minerals, structural intersections. The CV part is just doing this faster and across larger areas than a human team possibly could.

A good model can scan 10,000 km² of terrain in roughly 40 minutes. A field geologist working the same area? Six months minimum, and that's if the weather cooperates.

But — and this is the part the pitch decks skip — the model is only as good as the training data behind it. If you trained on Andean porphyry copper systems and then deploy it in the Bela ophiolite belt, you're going to get nonsense. The geology doesn't care about your loss function.

I got this wrong at first. Honestly. Our early models in 2023 overfit on Reko Diq-style signatures because that's what we had the most labeled data for. We ran a scan over a chromite-prospective zone in Muslim Bagh and the model flagged copper alteration everywhere. Total mess. Took us four months to rebuild the training set with proper ophiolite-hosted chromite signatures.

Where the Marketing Lies Start

Here's the thing about "AI mineral detection" claims you'll see on LinkedIn.

Most of them are doing one of three things:

1. Running a basic NDVI or band ratio (which any undergrad geology student can do in QGIS) and calling it AI
2. Using a generic image segmentation model trained on photos, not multispectral data
3. Showing you results from a known deposit and pretending it was "discovered" by their system

That last one is the worst. If your model "found" Saindak after being trained on data that included Saindak — congratulations, you've built a very expensive memorization machine.

Real computer vision in geomining has to do three hard things at once. It needs to fuse data from different sensors (Sentinel-2 for surface mineralogy, ASTER for thermal infrared, SAR for structural mapping, SRTM for topography). It needs to handle the fact that 70% of mineralization is hidden under cover, vegetation, or weathering. And it needs to output something a field team can actually drill.

None of that fits on a slide.

What We've Learned Running This in Pakistan

I own 15 mines in Gilgit Baltistan. So when I say I've tested this stuff on real ground, I mean my own money is on the table when the model is wrong.

A few things that surprised me:

SAR-based structural analysis is more valuable than the spectral stuff, most days. Everyone obsesses over Sentinel-2 band ratios. But in our marble and granite work near Chillas, the fracture density maps from Sentinel-1 SAR were what actually predicted the high-yield blocks. Mineral signatures tell you what's there. Structure tells you whether you can mine it economically. Two different questions.

Gravity survey data, when we can get it, beats almost everything. CV models trained on integrated gravity + multispectral data outperform spectral-only models by a wide margin for porphyry targeting. The problem is gravity data in Pakistan is patchy and often classified. So we work around it.

Ground truth changes everything. A model that gets 70% accuracy in a lab gets maybe 35% in the field on the first run. Then you walk the ground, you collect samples, you feed those samples back into the training loop, and accuracy climbs. AI geological mapping is a feedback loop, not a one-shot prediction.

And look, sometimes the model just sees something a geologist would've missed. We had a case in Khuzdar last year where our system flagged a zone with weak but unusual hydroxyl signatures that the local team had walked past for years. Turned out to be a small but high-grade lead-zinc occurrence. The model didn't "discover" it — the rocks were always there — but it pointed a finger at coordinates no one was looking at.

That's the realistic value. Not magic. Just attention, directed by math, at scale.

The Question Worth Asking

If you're a mine owner or an investor looking at any computer vision mining product — including ours at geomines.org — the question isn't "how accurate is your AI?"

The accuracy number is meaningless without context. Accurate on what deposit type? In what terrain? With what ground-truth dataset behind it? And what's the false positive rate, because that's the number that actually costs you money when you send a drill rig to the wrong coordinates.

Ask for the failure cases. A vendor who can't tell you where their model breaks is a vendor who hasn't tested it enough.

The technology is real. It works. We use it every day on actual licenses across Balochistan, KPK, and GB. But it's a tool that makes good geologists faster, not a replacement for thinking about the rocks.

Anyone selling you the second version is selling you a slide deck.