S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 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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3D Change Detection of Urban Construction Waste Accumulations Using Unmanned Aerial Vehicle Photogrammetry [PDF]

Qiang Chen, Yuan-Yuan Li, Zi-Yi Jia, and Qian-Hao Cheng

(Received May 29, 2021; Accepted July 12, 2021)

Keywords: construction waste, UAV photogrammetry, 3D change detection, remote sensing

Due to the large annual production of construction waste in China, the rapid changes in construction waste materials, and the need to quickly obtain high-precision 3D change information for construction waste, we have used a rapid monitoring method for construction waste accumulations, which uses unmanned aerial vehicle (UAV) photogrammetry to obtain a digital surface model (DSM) and a digital orthophoto map (DOM) of two study areas located in Pingdingshan City, China. Then, we built 3D models of waste accumulations in 2019 and 2020 for the two study areas. We also proposed a 3D change detection model combined with self-adaptive roughness quantification and supervised classification based on the elevation threshold to extract construction waste accumulations, and the extraction accuracies in 2019 and 2020 were 84.9% and 85.6%, respectively. Also, we used the adaptive threshold method and the discrete integral method to calculate the changes in areas and volumes of the accumulations. The calculation accuracy of the volume of construction waste reached 88%. This study provides a method for quickly obtaining 3D change information for construction waste accumulations.

Corresponding author: Yuan-Yuan Li




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