Phantom-fs/Rock-Type-Classification
Code and supplementary materials for the research paper titled 'Advancing Geological Image Segmentation: Deep Learning Approaches for Rock Type Identification and Classification', published in Applied Computing and Geosciences (Elsevier).
This project helps geologists and geological researchers automatically identify and classify rock types from images. By feeding in geological images, it outputs accurate classifications for 19 distinct rock types. This is ideal for professionals who need to quickly and reliably categorize rock samples for surveys, research, or resource exploration.
No commits in the last 6 months.
Use this if you need to rapidly and accurately classify a large volume of geological images into specific rock types.
Not ideal if you require identification of rock types beyond the 19 included in the dataset or need to process non-image-based geological data.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 19, 2024
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