Petrography is a branch of geology that analyzes the mineralogical composition of
rocks from microscopical thin section samples. It is essential for understanding
rock properties across geology, archaeology, engineering, mineral exploration, and
the oil industry. However, petrography is a labor-intensive task requiring experts
to conduct detailed visual examinations of thin section samples through optical
polarization microscopes, thus hampering scalability and highlighting the need for
automated techniques. To address this challenge, we introduce the Large-scale
Imaging and Thin section Optical-polarization Set (LITHOS), the largest and most
diverse publicly available experimental framework for automated petrography.
LITHOS includes 211,604 high-resolution RGB patches of polarized light and
105,802 expert-annotated grains across 25 mineral categories. Each annotation
consists of the mineral class, spatial coordinates, and expert-defined major and
minor axes represented as intersecting vector paths, capturing grain geometry and
orientation. We evaluate multiple deep learning techniques for mineral classifica-
tion in LITHOS and propose a dual-encoder transformer architecture that integrates
both polarization modalities as a strong baseline for future reference. Our method
consistently outperforms single-polarization models, demonstrating the value of
polarization synergy in mineral classification. We have made the LITHOS Bench-
mark publicly available, comprising our dataset, code, and pretrained models, to
foster reproducibility and further research in automated petrographic analysis.