The developed method was robust to inter-observer variability and produced very good accuracy — 3.2±1.1 mm absolute surface positioning error, <1 mm mean signed error and <5% mean volume difference.
Additionally, the MAE and mean signed error (MSE) of each gender and age group in 5-year intervals, which are counted based on the ground truth age and estimated age, respectively, are shown in Figs. 7 and 8, respectively.
We compared GNSS and lidar DTM elevations for ground reference locations using mean signed error, absolute error, and root mean square error (RMSE) [21].
First, we calculated the mean signed error (MSE) in degrees for the wind direction estimates.
The GNSS system parameters and measurement conditions during the survey are summarized in Table 3.> We compared GNSS and lidar DTM elevations for ground reference locations using mean signed error, absolute error, and root mean square error (RMSE) [ 21].
The error analysis of elevations using all 35 valid control points resulted in a mean signed error of 0.19 ± 0.97 m, and the calculated RMSE value was 0.97 m.
Moreover, using only the 30 most accurate control points (σ < 1 m) for comparison, the mean signed error dropped by 63% to 0.07 ± 0.89 m difference of terrain elevations.
Mean absolute difference is the absolute difference between predicted and measured values, without its sign, and is an indication of the magnitude of the error, whereas mean signed difference indicates whether a model tends to predict higher or lower values than the measured value.
To evaluate the performance of the spatial modeling algorithms, we used three statistical measures to evaluate the CV test results: the coefficient of determination (R2), the root-mean-square error (RMSE), and the mean signed deviation (MSD).
We evaluated methods using the mean signed difference, the mean absolute (unsigned) difference, and the mean squared error.
