Data Quality

Positional Accuracy

Positional accuracy is an assessment of the closeness of the location of the spatial objects in relation to their true positions on the earth’s surface. Positional accuracy consists of 2 assessments:

  • Horizontal accuracy assessment, and

  • Vertical accuracy assessment.

The horizontal and vertical positional accuracy is the assessed accuracy after all transformations have been carried out.

Horizontal Accuracy

The horizontal positional accuracy of Trees data reflects the positional accuracy of source sensors utilised in data collection, and the reliability of feature classification and associated orthogonalisation processes. The horizontal positional accuracy of source imagery varies across collected strips and ranges from +/-0.5m CE90 to +/-2.5m CE90.

Vertical Accuracy

Source elevation data used for the derivation of Trees height attributes is a digital surface model (DSM). Absolute spatial accuracy of the DSM ranges from +/-0.34m to +/-2m Linear Error 90% (LE90), +/-0.2m to +/-2m CE90 with relative accuracies of +/-1m LE90, +/-1m CE90.

Multiple factors can impact the quality of the assigned elevation or height, these include but are not limited to:

  • Age of source imagery

  • Correct classification of the feature

  • The off-nadir angle of source imagery

  • The omission of the feature: Where surface cover is not captured it cannot be assigned a height.

Thematic Quality

Thematic accuracy is defined as the accuracy of quantitative attributes, the correctness of non-quantitative attributes, and of the classification of features and their relationships.

Classification Correctness

Classification correctness is an assessment of the reliability of values assigned to features in the dataset in relation to their true ‘real world’ values.

Tree Theme

The rate of classification correctness of the Trees dataset has been measured at above 90%.

Logical Consistency

Logical consistency is a measure of the degree to which data complies to a technical specification. The test procedures are a mixture of software scripts and manual visual analysis. The data structure of Trees has been tested for conformance to the data model. The following have been tested and confirmed to conform:

  • File names

  • Attribute names

  • Attribute types

  • Attribute domains

  • Object type

Topological Consistency

Topological consistency is the measure of how features spatially relate to other features within and across the Trees theme. Topological inconsistencies are identified using a combination of automated rules, and visual analysis. Where topological inconsistencies are identified, they are notified back to the supplier for remediation. Some minor topological inconsistencies are corrected during product processing. The level of topological consistency is dependent on the data supplied to Geoscape.

Temporal Accuracy

Temporal accuracy is an assessment of both temporal consistency (how well-ordered lifecycle events are) and temporal validity (validity of data with respect to time).

Completeness

Completeness is an assessment of the extent and range of the dataset with regard to completeness of coverage, completeness of classification and completeness of verification. Components that makeup Trees includes Dataset, Theme, and Layer Coverage and coverage will be 100% complete across the areas captured to date. The Trees product contains a complete population of the Trees layer.

Attribute Completeness

The layer within the Trees have a full population of attributes in accordance with the data model.

Feature Completeness

The omission rate of Trees is directly related to the classification correctness of the Trees 2M and the vertical accuracies of the DSM and DTM.

Data Quality Scope

All spatial features including their attributes in the current time period for the Trees Dataset.