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Reflected Light Microscopic Iron ore image dataset for iron ore characterization.

Shama Firdaus¹, Shamama Anwar¹, Subrajeet Mohapatra¹

¹Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India.

|June 19, 2025

Summary

This dataset provides 563 reflected light microscopic images of iron ores to advance computer vision applications in mineral processing. The goal is to automate chemical-extensive tasks, improving ore analysis and quality assessment.

Keywords:

Computer vision Iron ore Microscopic images Minerals

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Area of Science:

Mineralogy
Materials Science
Computer Vision

Background:

Reflected light microscopy (RLM) is crucial for analyzing iron ore properties.
Current methods for ore analysis can be labor-intensive and time-consuming.

Purpose of the Study:

To introduce a comprehensive dataset of 563 RLM images of iron ores.
To facilitate the development of computer vision models for automated ore analysis.
To support research in mineral processing and resource characterization.

Main Methods:

Collection of 563 RLM images from various Indian iron ore mines.
Organization of images into 'IronOreRLM' and 'Sample Images' folders.
Dataset designed for computer vision-based analysis.

Main Results:

A valuable dataset for studying iron ore characteristics (elemental composition, quality, structure).
Enables quantitative analysis of ore properties through image data.
Demonstrates potential for automating chemical-extensive tasks.

Conclusions:

The IronOreRLM dataset is a significant resource for advancing automated mineral processing.
Computer vision techniques applied to this dataset can enhance ore exploration and quality control.
Further research can leverage this dataset for diverse mineralogical studies.