TY - GEN
T1 - Effects of random measurement errors on a linear dem error model
T2 - 2nd International Conference in Sustainable Buildings and Structures, ICSBS 2019
AU - Fan, L.
AU - Gong, G.
AU - Zhang, C.
N1 - Publisher Copyright:
© 2020 Taylor & Francis Group, London.
PY - 2020
Y1 - 2020
N2 - Terrestrial laser scanning (TLS) has now become an important tool for monitoring ground surface movements during and after construction. To better understand surface changes measured, it is important to have a sound understanding of digital elevation model (DEM) accuracy. A recent study showed that a linear model can be used to represent the digital elevation model (DEM) error in terms of root mean square error (RMSE) for some typical data spacing of TLS point clouds. However, the effects of the measurement noise on that model is not clearly understood. In this study, the measurement noise, as a controlled parameter, is added to data points in a semi-artificial point cloud that is assumed to free of measurement errors to form various new measurement-error-contaminated point cloud datasets. These new datasets were analysed using a statistical resampling technique, with an attempt to quantitatively investigate the effects of random measurement errors on the coefficients of the linear model.
AB - Terrestrial laser scanning (TLS) has now become an important tool for monitoring ground surface movements during and after construction. To better understand surface changes measured, it is important to have a sound understanding of digital elevation model (DEM) accuracy. A recent study showed that a linear model can be used to represent the digital elevation model (DEM) error in terms of root mean square error (RMSE) for some typical data spacing of TLS point clouds. However, the effects of the measurement noise on that model is not clearly understood. In this study, the measurement noise, as a controlled parameter, is added to data points in a semi-artificial point cloud that is assumed to free of measurement errors to form various new measurement-error-contaminated point cloud datasets. These new datasets were analysed using a statistical resampling technique, with an attempt to quantitatively investigate the effects of random measurement errors on the coefficients of the linear model.
UR - http://www.scopus.com/inward/record.url?scp=85108920090&partnerID=8YFLogxK
U2 - 10.1201/9781003000716-45
DO - 10.1201/9781003000716-45
M3 - Conference Proceeding
AN - SCOPUS:85108920090
SN - 9780367430191
T3 - Sustainable Buildings and Structures: Building a Sustainable Tomorrow - Proceedings of the 2nd International Conference in Sustainable Buildings and Structures, ICSBS 2019
SP - 329
EP - 334
BT - Sustainable Buildings and Structures
A2 - Papadikis, Konstantinos
A2 - Chin, Chee S.
A2 - Galobardes, Isaac
A2 - Gong, Guobin
A2 - Guo, Fangyu
PB - CRC Press/Balkema
Y2 - 25 October 2019 through 27 October 2019
ER -