公开12714张土壤图像及粒径分布数据，助力用视觉分析替代传统实验室检测。
Dataset of soil images with corresponding particle size distributions for photogranulometry
- 采集321种蒙特利尔土壤样本，每份拍干湿两态高清图。
- 图像分辨率4500万像素，最小分辨率达39.4微米/像素。
- 适合做地质工程中基于图像的粒径分析模型训练。
传统粒径分布（PSD）分析耗时且成本高。本文提出一个高分辨率数据集，包含来自魁北克蒙特利尔地区321种土壤样本的12,714张图像及其对应的PSD分析结果。所有样本均在标准俯视位置拍摄，分辨率为45兆像素，最小尺度为39.4微米/像素，涵盖干、湿两种状态。使用定制测试台（13x9英寸白色铝盘）将样品铺成薄层，对超限样本采用锥形四分法减量处理。该数据集旨在为地质工程中卷积神经网络（CNN）的训练提供坚实基础。
原文摘要 · Abstract (English)
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.
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