A smote–vit framework for advanced soil classification on a self-generated geotechnical image database

Abstract

Accurate soil type classification is fundamental to geotechnical engineering, yet traditional laboratory methods are often time consuming and labor intensive. This study investigates the potential of a Transformer-based deep learning framework for the automated classification of complex soil compositions. An image database for geotechnical analysis is constructed using six distinct geotechnical samples comprising gravel, sand, silt, and clay systematically blended into 80 ternary mixtures. To address the inherent class imbalances in the multi-component dataset, the Synthetic Minority Oversampling Technique (SMOTE) is employed, ensuring robust representation across all categories. The proposed framework utilizes a Vision Transformer (ViT) architecture, leveraging its self-attention mechanism to capture both intricate textural patterns and long-range structural dependencies within the soil matrices. Experimental results demonstrate that the SMOTE–ViT pipeline achieved an overall accuracy of 95.83%, with high precision and recall across diverse ternary compositions. This interdisciplinary approach provides a scalable and high-precision alternative for soil characterization, offering significant potential for real-time decision-making in geotechnical investigation workflows.