Dataset of soil images with corresponding particle size distributions for photogranulometry

TL;DR

This paper introduces a large, high-resolution soil image dataset with corresponding particle size distributions, aimed at training CNNs to improve optical grain size analysis in geotechnical labs, reducing costs and downtime.

Contribution

It provides a comprehensive soil image dataset with PSD labels, facilitating machine learning applications in geotechnical analysis and advancing optical grain size measurement methods.

Findings

Dataset of 12,714 images of 321 soil samples

High-resolution images with standardized photography setup

Dataset suitable for training CNNs for soil analysis

Abstract

Traditional particle size distribution (PSD) analyses create significant downtime and are expensive in labor and maintenance. These drawbacks could be alleviated using optical grain size analysis integrated into routine geotechnical laboratory workflow. This paper presents a high-resolution dataset of 12,714 images of 321 different soil samples collected in the Montreal, Quebec region, alongside their PSD analysis. It is designed to provide a robust starting point for training convolutional neural networks (CNN) in geotechnical applications. Soil samples were photographed in a standardized top-view position with a resolution of 45 MP and a minimum scale of 39.4 micrometers per pixel, both in their moist and dry states. A custom test bench employing 13x9 inch white aluminum trays, on which the samples are spread in a thin layer, was used. For samples exceeding a size limit, a coning and quartering method was employed for mass reduction.