High-Resolution Soil Image Dataset with Corresponding Particle Size Distributions for Photogranulometry

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 the largest high-resolution dataset of its kind, with 12,714 images of 321 different soil samples collected in the Montreal, Quebec region, alongside their PSD analysis, including hydrometer results for 77 samples. 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 µm per pixel, both in their moist and dry states. A custom test bench employing 13″×9″ 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 was employed for mass reduction.