VerticalAccuracy
|
Line |
Component |
Measure Description |
|
1 |
Name |
absolute linear error at 90 % significance level of biased vertical data |
|
2 |
Alias |
LMAS |
|
3 |
Data quality element |
Absolute or External Positional Accuracy |
|
4 |
Data quality basic measure |
|
|
5 |
Definition |
absolute vertical accuracy of the data’s coordinates, expressed in terms of linear error at 90 % probability given that a bias is present |
|
6 |
Description |
See STANAG 2215 ed7 Appendix 2 |
|
7 |
Parameter |
|
|
8 |
Data quality value type |
Measure |
|
9 |
Data quality value structure |
|
|
10 |
Source reference |
NATO STANAG 2215 ed7 ISO 19157 measure n°40 |
|
11 |
Example |
|
|
12 |
Identifier |
|
Line |
Component |
Description |
|
1 |
Name |
absolute linear error at 90 % significance level of biased vertical data (Alternative 2) |
|
2 |
Alias |
ALE |
|
3 |
Element name |
absolute or external accuracy |
|
4 |
Basic measure |
not applicable |
|
5 |
Definition |
absolute vertical accuracy of the data’s coordinates, expressed in terms of linear error at 90 % probability given that a bias is present |
|
6 |
Description |
A comparison of the data (source) and the control (reference) is calculated in the following manner: 1. Calculate the absolute error in the vertical dimension at each point: δ V i = source V i − reference V i for i = 1 … N 2. Calculate the mean vertical error: | δ V ¯ | = | 1 N ∑ i = 1 N δ V i | 3. Calculate the standard deviation of the vertical errors: σ V = 1 N ∑ i = 1 N δ V i 2 4. Calculate the ratio of the absolute value of the mean error to the standard deviation: ratio = | δ V ¯ | / σ V 5. If ratio > 1 , 4 , then k = 1 , 2815 6. If ratio ≤ 1,4, then calculate k based on the ratio of the vertical bias to the standard deviation of the heights using a cubic polynomial fit through the tabular values as defined in the Handbook of Tables for Probability and Statistics (Reference[ 20 ]). k = 1 , 643 5 − ( 0 , 999 556 × ratio ) + ( 0 , 923 237 × ratio 2 ) − ( 0 , 282 533 × ratio 3 ) 7. Compute LE90 for the source: LE90 source = | δ V ¯ | + ( k × σ V ) 8. Compute absolute LE90: LE90 abs = LE90 reference 2 + LE90 source 2 |
|
7 |
Parameter |
Name: Sample size Definition: minimum of 30 points is normally used but may not always be possible depending on identifiable control points. For feature level attribution sample 10 % of the feature population. Value Type: Real |
|
8 |
Value type |
Measure |
|
9 |
Value structure |
- |
|
10 |
Source reference |
ISO 19157 n°41 1. Mapping, Charting and Geodesy, Accuracy (Reference[ 21 ]) 2. Handbook of Tables for Probability and Statistics (Reference[ 20 ]) 3. NATO STANAG 2215 IGEO (Reference[ 22 ]) |
|
11 |
Example |
- |
|
12 |
Measure identifier |
|
Line |
Component |
Measure Description |
|
1 |
Name |
absolute linear error at 90 % significance level of biased vertical data (small sample) |
|
2 |
Alias |
LMAS |
|
3 |
Data quality element |
Absolute or External Positional Accuracy |
|
4 |
Data quality basic measure |
|
|
5 |
Definition |
absolute vertical accuracy of the data’s coordinates, expressed in terms of linear error at 90 % probability given that a bias is present |
|
6 |
Description |
See STANAG 2215 ed7 Appendix 2 |
|
7 |
Parameter |
Sample size |
|
8 |
Data quality value type |
Measure |
|
9 |
Data quality value structure |
|
|
10 |
Source reference |
NATO STANAG 2215 ed7 ISO 19157 measure n°41 |
|
11 |
Example |
|
|
12 |
Identifier |
