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, flight heights, and image overlaps. This study investigated 47 stockpiles across two distinct sites, including sand and gravel materials. Using Pix4D photogrammetry, 3D models were generated, with individual stockpile volumes ranging from 51 m³ to 3000 m³. Data was collected during multiple surveys; however, stockpiles were not individually tracked across time, so the dataset should be regarded as cross-sectional rather than strictly longitudinal. Stockpile volumes in one of the sites changed overtime during the data collection. The dataset was enriched with annotated 3D points identifying not only stockpiles, but irrelevant objects, such as trees, vehicles, and roads. The point clouds generated from these models were annotated in PLY and XYZ formats, creating a unique 3D point dataset with corresponding 2D images. This dataset is well-suited for the development of autonomous detection and measurements of objects using 3D deep learning models for object detection. Engineering (Construction, Mining, Aerospace, Computer Science, Agriculture) Civil Engineering. Raw 2D images and annotated 3D point cloud. DJI Phantom 4 Pro was employed to collect images of regular and irregular objects. The UAS is compatible with Pix4D, allowing for autonomous path planning. Pix4D was utilized to conduct autonomous flights and generate 3D models by setting flight parameters such as altitude, overlapping, and flight mission. The software also facilitated image stitching, 3D model construction, and the creation of annotated datasets. University of North Dakota Stockpiles site: Tech Accelerator, Address: 4201 James Ray Dr, Grand Forks, ND 58202.
Jafari et al. (Sun,) studied this question.