The statistical summary values at the bottom of the Control Points Report, opened by pressing the Accuracy Estimation tool on the Control Points tab of the ribbon, display the statistics and the ASPRS (American Society for Photogrammetry and Remote Sensing) accuracy assessment panel necessary to determine the accuracy of the LIDAR surface used in the report. The values in the "Error" column of the Control Point List are used to produce the statistics, and points that are unchecked in the list are excluded from the statistics.
Figure 1: Statistics tab on the Control Points Report
Figure 2: ASPRS tab on the Control Points Report
📊 Statistics
The statistics are grouped into five main categories:
1. X and Y (Horizontal Accuracy)
| Metric | Value (X) | Value (Y) | Definition |
|---|---|---|---|
| RMSE | 0.035 | 0.027 | Root Mean Square Error: Measures the average magnitude of the positional error. |
| Mean Error | 0.008 | 0.004 | The average signed error (bias) between measured and reference points. |
| SDOM | 0.007 | 0.006 | Standard Deviation of the Mean: Indicates the precision of the mean error. |
| Sx / Sy | 0.034 | 0.027 | Standard deviation of the X or Y errors, showing the spread of error values. |
| Error Min, Max | [-0.053, 0.096] | [-0.079, 0.051] | The smallest and largest observed errors. |
| Error Range | 0.150 | 0.130 | Difference between the maximum and minimum errors. |
2. Planimetric (2D Horizontal Accuracy Combined)
| Metric | Value | Definition |
|---|---|---|
| RMSE | 0.044 | Combined horizontal RMSE from X and Y. |
| Mean Error | 0.039 | Average horizontal error. |
| SDOM | 0.005 | Precision of the mean horizontal error. |
| Sxy | 0.021 | Standard deviation of combined X and Y errors. |
| Error Min, Max | [0.002, 0.097] | Range of horizontal errors. |
| Error Range | 0.095 | Spread of horizontal error values. |
3. Three Dimensions (3D Accuracy)
| Metric | Value | Definition |
|---|---|---|
| RMSE | 0.047 | Root Mean Square Error in 3D space. |
| Mean Error | 0.043 | Average 3D positional error. |
| SDOM | 0.004 | Precision of the mean 3D error. |
| Sxyz | 0.020 | Standard deviation of 3D errors. |
| Error Min, Max | [0.016, 0.098] | Range of 3D errors. |
| Error Range | 0.083 | Spread of 3D error values. |
4. Vertical (Z Accuracy)
| Metric | Value | Definition |
|---|---|---|
| RMSE | 0.015 | Root Mean Square Error in the vertical (Z) direction. |
| Mean Error | -0.011 | Average vertical error (bias). |
| SDOM | 0.0002 | Precision of the mean vertical error. |
| Sz | 0.0111 | Standard deviation of vertical errors. |
| Error Min, Max | [-0.0300, -0.0013] | Range of vertical errors. |
| Error Range | 0.0043 | Spread of vertical error values. |
🧭 ASPRS Tab Overview
The ASPRS tab in LP360 provides a structured summary of error components and accuracy classifications based on ASPRS standards. It includes three main sections:
🔹 Components of Error
| Category | RMSE(X) | RMSE(Y) | RMSE(Z) | RMSE(XY) | RMSE(XYZ) |
|---|---|---|---|---|---|
| Product Fit (1st) | 0.035 | 0.027 | 0.015 | 0.044 | 0.047 |
| Survey Control (2nd) | 0.020 | 0.020 | 0.032 | 0.028 | 0.043 |
| Product Accuracy | 0.040 | 0.034 | 0.035 | 0.052 | 0.064 |
Definitions:
- Product Fit (1st): Internal consistency of the point cloud.
- Survey Control (2nd): Accuracy relative to known ground control points. Learn how to Set Survey Control Error. Learn How to Determine Survey Error for ASPRS 2024 Accuracy Reporting.
- Product Accuracy: Final accuracy of the dataset after all adjustments.
🔹 Summary
| Metric | Value | Definition |
|---|---|---|
| ASPRS Vertical Accuracy Class | 0.035 | Classification based on vertical RMSE. |
| ASPRS Horizontal Accuracy Class | 0.052 | Classification based on horizontal RMSE. |
| Equivalent Class I Contour Interval | 0.105 | Derived contour interval corresponding to vertical accuracy class. |
📌 Statistical Point Counts
| Metric | Value | Definition |
|---|---|---|
| Horizontal Measured | 21 | Number of horizontal check points used. |
| Vertical Measured | 34 | Number of vertical check points used. |
| Withheld | 24 of 45 | Points excluded from the accuracy assessment (e.g., for validation). |
🔧 Correction Parameters
| Parameter | Value | Definition |
|---|---|---|
| Ox, Oy, Oz | 0.0000 | Original offsets in X, Y, Z directions. |
| Tx, Ty, Tz | 0.0008, 0.0004, -0.0014 | Translation corrections applied to align the dataset. |
| Rx, Ry, Rz | (not used in this example) | Rotation corrections (Optional). |
📘Reference
Mean Error (errorm):
The sum of the vertical errors divided by the number of errors (n).
Error Min, Max:
The minimum and maximum vertical error used in the calculation of the mean error.
Error Range:
The Error Range computed as |Xmax - Xmin| from the 'Error Min, Max' above.
RMSEz:
The root mean squared error for the errors.
√ [ ∑ in (errori2)/n]
NMAS/VMAS Accuracyz (90% CI):
The vertical accuracy of the surface for 90% confidence level.
RMSEz × 1.645
ASPRS/NSSDA Accuracyz (95% CI):
The vertical accuracy of the surface for 95% confidence level.
RMSEz × 1.96
🧮 ASPRS Accuracy Classes Explained
ASPRS accuracy classes are standardized thresholds used to evaluate the positional accuracy of geospatial data. These classes are defined in the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) and are used to ensure consistency across datasets.
🔸 Vertical Accuracy Classes
Vertical accuracy is typically assessed using RMSE(Z) and is categorized as follows:
| Class | RMSE(Z) (meters) | Description |
|---|---|---|
| Class 1 | ≤ 0.064 | High-accuracy data (e.g., for engineering or flood mapping) |
| Class 2 | ≤ 0.128 | Moderate-accuracy data |
| Class 3 | ≤ 0.192 | Lower-accuracy data |
In the example, the Vertical Accuracy Class is 0.035, which qualifies as Class 1.
🔸 Horizontal Accuracy Classes
Horizontal accuracy is assessed using RMSE(XY) and categorized similarly:
| Class | RMSE(XY) (meters) | Description |
|---|---|---|
| Class 1 | ≤ 0.064 | High-accuracy horizontal data |
| Class 2 | ≤ 0.128 | Moderate-accuracy horizontal data |
| Class 3 | ≤ 0.192 | Lower-accuracy horizontal data |
In the example, the Horizontal Accuracy Class is 0.052, also qualifying as Class 1.
🔸 Equivalent Contour Interval
This value estimates the smallest contour interval that can be reliably derived from the vertical accuracy. It is calculated using:
Contour Interval ≈ 3×RMSE(Z)
In this case:
3×0.035=0.105 meters 0.035 0.105 meters
This means the data supports 0.105-meter contours with Class 1 confidence.
Please use the following links to learn more about the NMAS/VMAS and ASPRS/NSSDA standards:
American Society for Photogrammetry and Remote Sensing (ASPRS)
Comments
0 comments
Please sign in to leave a comment.