HorizontalAccuracy

Absolute accuracy

Line	Component	Measure Description
1	Name	absolute circular error at 90 % significance level of biased data
2	Alias	CMAS
3	Data quality element	Absolute or External Positional Accuracy
4	Data quality basic measure
5	Definition	absolute horizontal accuracy of the data’s coordinates, expressed in terms of circular 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 ISO 19157 measure n°48
11	Example
12	Identifier	http://dgiwg.org/metadata/qualityMeasure/CMAS

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Line	Component	Measure Description
1	Name	absolute circular error at 90 % significance level of biased data
2	Alias	ACE
3	Element name	absolute or external accuracy
4	Basic measure	not applicable
5	Definition	absolute horizontal accuracy of the data’s coordinates, expressed in terms of circular 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 horizontal dimension at each point: Δ H i = ( source X i − reference X i ) 2 + ( source Y i − reference Y i ) 2 for i = 1…N 2. Calculate the mean horizontal error: μ H = ( ∑ Δ H i ) N 3. Calculate the standard deviation of the horizontal errors: σ H = ∑ ( Δ H i − μ H ) 2 ( N − 1 ) 4. Calculate the ratio of the absolute value of the mean error to the standard deviation: ratio = | μ H | / σ H 5. If ratio > 1 , 4 , then k = 1 , 281   5 6. If ratio ≤ 1,4, then calculate k, the ratio of the mean to the standard deviation, using a cubic polynomial fit through the tabular values as defined in the CRC Handbook of Tables for Probability and Statistics [ 20 ] k = 1 , 643   5 − ( 0 , 999   556 × ratio ) + ( 0 , 923   237 × ratio 2 ) − ( 0 , 282   533 × ratio 3 ) 7. Compute CE90 for the source: CE90 source = | μ H | + ( k × σ H ) 8. Compute absolute CE90: CE90 abs = CE90 reference 2 + CE90 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 Measure n° 49 1. Mapping, Charting and Geodesy Accuracy (Reference[ 21 ]) 2. Handbook of Tables for Probability and Statistics (Reference[ 20 ])
11	Example	-
12	Identifier	http://dgiwg.org/metadata/qualityMeasure/ACE

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Line	Component	Measure Description
1	Name	Cartography of quality zones
2	Alias	CQZ
3	Data quality element	Absolute or External Positional Accuracy
4	not applicable
5	Definition	Cartography of horizontal quality zones (defined by their boundaries in GML or Shapefile)
6	Description	Information related to the result of a quality evaluation of the dataset on absolute horizontal accuracy (CE90) covResult : - format = GML (version 3.3) or SHP (version 1.0) - geometry = surface - content = (contentType=’qualityInformation’) - file = (name=’link to the GML or SHP file) - and (description=”Cartography of quality zones”)
7	Parameter
8	Data quality value type	GM_Surface
9	Data quality value structure	coverage
10	Source reference	http://dgiwg.org/metadata/qualityMeasure/ACE
11	Example
12	Identifier	http://www.dgiwg.org/metadata/qualityMeasure/CQZ

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Relative accuracy

Line	Component	Measure Description
1	Name	Point-to-point horizontal accuracy
2	Alias	RelCE90
3	Data quality element	Relative or internal accuracy
4	Data quality basic measure
5	Definition	Uncertainty in the difference in horizontal positions between any 2 points. The value is expressed as a circular error at the 90% confidence level.
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
11	Example
12	Identifier	http://dgiwg.org/metadata/qualityMeasure/RelCE90

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Line	Component	Measure Description
1	Name	US National Image Interpretability Rating Scales
2	Alias	NIIRS
3	Data quality element	Relative or Internal accuracy
4	Data quality basic measure
5	Definition	The aerial imaging community utilizes the National Imagery Interpretability Rating Scale (NIIRS) to define and measure the quality of images and performance of imaging systems. Through a process referred to as "rating" an image, the NIIRS is used by imagery analysts to assign a number which indicates the interpretability of a given image. The NIIRS concept provides a means to directly relate the quality of an image to the interpretation tasks for which it may be used. Although the NIIRS has been primarily applied in the evaluation of aerial imagery, it provides a systematic approach to measuring the quality of photographic or digital imagery, the performance of image capture devices, and the effects of image processing algorithms.
6	Description	See https://fas.org/irp/imint/niirs.htm
7	Parameter
8	Data quality value type	Measure
9	Data quality value structure
10	Source reference	https://fas.org/irp/imint/niirs.htm -
11	Example	5
12	Identifier	http://dgiwg.org/metadata/qualityMeasure/NIIRS

This quality result has been created based on feature level metadata

Line	Component	Measure Description
1	Name	Survey Coverage Categories rate
2	Alias	SUR
3	Data quality element	Position Accuracy
4	not applicable
5	Definition	Survey Coverage Categories
6	Description	Percentage of each category of Survey Coverage. This quality result is based on surveyCoverageCategory value. - inadequatlySurveyed percentage: percentage of elements with "inadequatlySurveyed" category - surveyed percentage: percentage of elements with "surveyed" category - unsurveyed percentage: percentage of elements with "unsurveyed" category
7	Parameter
8	Data quality value type	egco:Record
9	Data quality value structure	3 parameters value: surveyed, unsurveyed and inadequatelySurveyed, each of type percentage.
10	Source reference
11	Example
12	Identifier	http://www.dgiwg.org/metadata/qualityMeasure/SUR