The accuracy of the outputs can be distinguished into:
- Relative accuracy: It is the accuracy that is defined by comparing individual features on a map / reconstructed model / orthomosaic with other features on the same model. For example, two points of the model can be 2 meters away from their real position on the earth but if their relative accuracy is high, then the distance measured between these points will be very accurate as it is related with the relative position of the points.
- Absolute accuracy: It is the accuracy that is defined by the difference between the location of features on a map / reconstructed model / orthomosaic and their true position on the Earth.
The relative accuracy will depend on the quality of the reconstruction of the project, which itself depends on the overlap between images, the visual content of the images, and many other parameters.
Generally, one can expect an error of 1-3 times the Ground Sampling Distance (GSD) of the original images for the relative position of a point in a project that is correctly scaled and reconstructed.
Using Ground Control Points (GCPs), the relative accuracy may improve (especially in areas with lower overlap or with difficult image content).
Absolute accuracy increases significantly when using RTK GPS for the image geolocation or when using GCPs. When using GCPs, the accuracy depends on the accuracy of the measured GCPs, their number and their distribution.
Generally, the expected accuracy of a correctly reconstructed project is:
- 1-2 GSD horizontally (X,Y coordinates).
- 1-3 GSD vertically (Z coordinate).
Point cloud, DSM, orthomosaic accuracy
The point cloud, DSM and, consequently, the orthomosaic accuracy are also affected by the quality of the initial images and their visual content. Sharp edges, trees, reflective surfaces, and certain type of roads and rooftops may be locally less accurate.
For more information about how to use the rayCloud to improve accuracy: How to use the rayCloud to improve the Accuracy?.