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List of resources for mineral exploration and machine learning, generally with useful code and examples.
| Date | Stars |
|---|---|
| 2026-07-31 | 333 |
| 2026-08-06 | 333 |
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# mineral-exploration-machine-learning This page lists resources for mineral exploration and machine learning, generally with useful code and examples. ML and Data Science is a huge field, these are resources I have found useful and/or interesting to me in practice. Links currently to a fork of a repository are because I have changed something to use and put in a list for reference. Resources are also given for data analysis, transformation and visualisation as that is most of the work. Suggestions welcome: open a discussion, issue or pull request. # Table of Contents * [Prospectivity](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#prospectivity) * [Geology](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#geology) * [Natural Language Processing](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#natural-language-processing) * [Remote Sensing](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#remote-sensing) * [Data Quality](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#data-quality) * [Community](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#communit) * [Cloud providers](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#cloud-providers) * [Domains](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#domains) * [Overview](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#overview) * [Web Services](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#web-services) * [Data Portals](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#data-portals) * [Tools](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#tools) * [Ontologies](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#ontologies) * [Books](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#books) * [Datasets](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#datasets) * [Papers](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#papers) * [Other](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#other) * [General Interest](https://github.com/RichardScottOZ/mineral-exploration-machine-learning#general-interest) # Map * [Map of Github](https://anvaka.github.io/map-of-github/#10.26/28.263/19.7499) * [DeepWiki](https://github.com/AsyncFuncAI/deepwiki-open) -> automagic wiki style analysis of a repo via llm # Frameworks * [UNCOVER-ML Framework](https://github.com/RichardScottOZ/uncover-ml) * [Geo-Wavelets](https://github.com/RichardScottOZ/geo-wavelets) * [ML-Preprocessing](https://github.com/GeoscienceAustralia/ML-preprocessing) * [Landshark](https://github.com/data61/landshark) -> Large-scale spatial inference with tensorflow * [GIS ML Workflow](https://github.com/sheecegardezi/GIS-ML-Workflow) * [EIS Toolkit](https://github.com/GispoCoding/eis_toolkit/tree/master) -> Python library for mineral prospectivity mapping from EIS Horizon EU Project * [PySpatialML](https://github.com/RichardScottOZ/Pyspatialml) -> Library that facilitates prediction and handling for raster machine learning automatically to geotiff, etc. * [DARPA Criticalmaas](https://github.com/DARPA-CRITICALMAAS) * [competition info](file:///C:/Users/rscott/Downloads/DARPA-PA-22-02-01.pdf) * [uncharted T1](https://github.com/DARPA-CRITICALMAAS/uncharted-ta1) -> Automated feature extraction and georeferencing of geologic maps * [paper](www.sciencedirect.com/science/article/pii/S2590197425000564) -> Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition * [training data](https://data.usgs.gov/datacatalog/data/USGS:63a100d6d34e0de3a1f2794f) * [QueryPlot](https://github.com/DARPA-CRITICALMAAS/sri-ta2-mappable-criteria) - Generating mineral evidence maps from geological que
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3045915cdd61155, llm:Repository description and README: 'List of resources for mineral exploration and machine learning... Resources are also given for data analysis, transformation and visualisation' and topics include geoscience, machine-learning, data-science, geology, remote sensing, nlp, prospectivity.
matched fp:e3045915cdd61155, llm:Repository description and README: 'List of resources for mineral exploration and machine learning... Resources are also given for data analysis, transformation and visualisation' and topics include geoscience, machine-learning, data-science, geology, remote sensing, nlp, prospectivity.
matched fp:e3045915cdd61155, llm:Repository description and README: 'List of resources for mineral exploration and machine learning... Resources are also given for data analysis, transformation and visualisation' and topics include geoscience, machine-learning, data-science, geology, remote sensing, nlp, prospectivity.