Dataset of soil images with corresponding particle size distributions for photogranulometry
This provides a resource for developing optical grain size analysis methods in geotechnical engineering, but it is incremental as it focuses on dataset creation rather than new algorithms.
The paper tackles the high cost and downtime of traditional particle size distribution analysis by creating a dataset of 12,714 high-resolution images of 321 soil samples with corresponding PSD data, designed to train CNNs for geotechnical applications.
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.