Granular stockpile volume dataset
Faezeh Jafari, Sattar Dorafshan

TL;DR
This paper introduces a comprehensive UAS dataset for measuring granular stockpile volumes using vision-based methods, supporting research in automation and accuracy.
Contribution
The paper presents a novel annotated UAS dataset for granular stockpile volume measurement with varied data collection parameters.
Findings
The dataset includes 1521 images and 3D models of 47 stockpiles with volumes ranging from 51 to 3000 m³.
Point clouds were annotated in PLY and XYZ formats, enabling 3D deep learning for object detection.
The dataset supports cross-sectional analysis of stockpiles under varying environmental and flight conditions.
Abstract
Unmanned Aerial Systems (UAS) applications are growing for vision-based volume measurement to enhance accuracy, efficiency, and automation. Despite the growing applications of UAS, no comprehensive dataset is currently available for researchers to determine the effects of visual data collection parameters such as camera angles, image overlaps, and flight patterns, on the outcomes. These outcomes consist of but are not limited to the number of images, the density of point clouds, and the quality of 3D models. This study introduces an annotated UAS dataset to allow researchers and practitioners to use vision-based UAS data for accurate measurement of granular stockpiles. Data were collected from stockpiles with irregular shapes in Grand Forks, ND, USA using UAS. The dataset includes 1521 images captured under varying weather conditions, stockpile sizes, camera angles, flight patterns,…
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Taxonomy
TopicsMineral Processing and Grinding · Granular flow and fluidized beds · Stochastic Gradient Optimization Techniques
