Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Digital Elevation Model Production Using Point Cloud Acquired by Unmanned Aerial Vehicles

Suk Bae Lee, Jae Ho Won, Kap Yong Jung, Mihwa Song, and Young Joon Ahn

(Received July 1, 2020; Accepted November 17, 2020)

Keywords: UAV, point cloud, DSM, DEM

Currently, point cloud data acquired by using unmanned aerial vehicles (UAVs) are mostly used for the production of digital surface models (DSMs). This paper shows the possibility of digital elevation model (DEM) production with point cloud data acquired using UAVs. In this study, which was conducted in Korea, we used 314 images acquired with a DJI Inspire-2 UAV. To extract ground data, we performed a point cloud auto-classification with six types of software, Pix4DMapper, GlobalMapper, Inpho, Trimble Business Center (TBC), Metashape, and Terrascan, and the results were compared. Pix4DMapper was used for point cloud extraction, and all six types of software used the default options for point cloud auto-classification. A point cloud acquired using LiDAR classifies vegetation using an echo, but a point cloud extracted from an image has no echo, making it impossible to classify vegetation using an echo. In this study, DEMs were produced using ground data classified automatically with the six types of software, and they were compared with a DEM produced by the Korean National Geographic Information Institute (NGII). As a result, the DEM error rate was 38–47% depending on the software. In a mountainous area, the dense forest made it impossible to extract ground data, resulting in a very high error rate of 82–92%.

Corresponding author: Jae Ho Won




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