Horizontal Error
Computes observed-minus-reference X/Y errors in a local metric coordinate system.
Δx=xobs−xref
Δy=yobs−yref
er=√(Δx²+Δy²)
Horizontal Accuracy Assessment
Assess orthophoto horizontal positional accuracy using independent check points. Each reference point is paired with the corresponding visible location in the orthophoto.
Accuracy & RectificationEnd-User DocumentationTheory + Formula + Parameters1. What This Feature Does
Assess orthophoto horizontal positional accuracy using independent check points. Each reference point is paired with the corresponding visible location in the orthophoto.
Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.
2. Recommended Workflow
Load an independent point layer.
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For each checkpoint, click the same object in the orthophoto.
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Complete matched pairs.
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Run the assessment.
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Review X/Y bias, radial error, RMSE, MAE and empirical CE percentiles.
3. Theory, Methods & Equations
RMSE
Summarizes quadratic error.
RMSEx=√[(1/n)ΣΔx²]
RMSEr=√[(1/n)Σer²]
CE50 / CE68 / CE90 / CE95
Empirical percentiles of radial horizontal error.
CEp=Percentilep(er)
Best used when: reporting a direct percentile-based circular error statistic.
4. Input Data
| Input | Type | Requirement | Description |
|---|---|---|---|
| Orthophoto | raster | Required | Orthophoto raster to be assessed. |
| ICP / Check Point Layer | vector | Required | Uploaded POINT check-point layer normalized to EPSG:4326. |
5. Parameters Available in the Application
| Parameter | Default | Choices / Range | How to Use It |
|---|---|---|---|
| Matched Check Points | — | — | Generated automatically by the map UI; no manual coordinates. |
6. Output & Interpretation
The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.
Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.
Important: Use independent check points not used to rectify/orthorectify the image. Reference and observed features must represent the same identifiable ground object.
7. Best Practices
- Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
- Choose parameters from the physical scale of the data, not only from visual appearance.
- Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
- Compare the result with the source image and independent reference information.
- Document the settings used when results will be compared across dates, sensors or study areas.