Loading a Scanned Geologic Map Into an MCP Agent
AI for geology often starts with a scanned map. How geomap_register_map checks allowed roots, formats and size, then returns a stable geomap:// map URI.

AI for geology often starts with a scanned map. How geomap_register_map checks allowed roots, formats and size, then returns a stable geomap:// map URI.

Before a geology AI agent calls a map tool, geomap_list_capabilities shows what is registered, installed, configured and ready, and what is still missing.
Acid rock drainage prediction with AI: run ABA and humidity cell tests per the GARD Guide, model results in PHREEQC, and see where machine learning fits.
Autonomous drilling with rigs like Epiroc Pit Viper and Sandvik AutoMine uses onboard navigation and MWD sensing to hit each hole collar within about 5 cm.
Grade control sensors such as XRF, XRD, LIBS and hyperspectral scanners rely on AI calibration software to turn raw spectra into grade and mineralogy.
Ore texture analysis with AI: run samples or core through TIMA, Mineralogic or Corescan for ML-classified grain size and mineral maps that size your grind.
Automated mineralogy on TIMA, MLA, QEMSCAN or Mineralogic uses ML classifiers to turn SEM X-ray spectra into modal mineralogy and liberation data in hours.
A drone survey in mining pairs LiDAR or photogrammetry (Hovermap SLAM where GPS fails) with Pix4D or DJI Terra and implicit modelling for 3D terrain.
Implicit geological modelling of lithology, structure and alteration in Leapfrog or Vulcan, with Seequent's Driver adding ML clustering and classification.
Blast design optimization with AI: feed drone bench surveys and past blasts into Strayos or O-Pitblast, then run the design in the field with Orica BlastIQ.