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Quality control and assurance

The Globhe standards for quality control and assurance on drone data deliverables

Globhe ensures high quality standards and perform in-depth quality control on the data captured and processed by the drone operators before sending to clients through the Globhe marketplace and utilising different software and resources. The process is standardised but also personalised for each data type and includes:

  • Checking that the data deliverables are in line with the client’s order and in the correct format
  • Ensuring that no personal photos, like drone selfies or images of the ground are excluded 
  • Verifying file size is the expected one from the drone model (i.e. for drone images: 12 MP RGB drone camera = typically 4.5 - 6 MB; and 20 MP = typically 7 - 8 MB)
  • Verifying overall image quality (ensuring that the images are clear, properly exposed) and consistency in terms of exposure/brightness and shadows
  • Verifying that the data is free from significant artefacts or distortions (i.e. saturated image, vignetting, chromatic aberration, mosaic blurring, hotspots, relief displacement effects, mosaic gaps)
  • Checking for sharpness and focus to ensure that the images are in focus and have sufficient detail
  • Evaluating colour accuracy and balance to ensure that the colours are natural and true to the scene
  • Check for noise or graininess, particularly in low-light conditions
  • Verifying that the data covers the area of interest and includes the required details 
  • Checking for any missing or incomplete parts of the scene that may affect the data's usefulness
  • Ensuring that the data are correctly georeferenced 
  • Verifying that the data aligns with other geospatial data, such as orthomosaic maps or GIS layers, if possible 
  • Inspecting the embedded metadata, including capture date and time, GPS/geolocation coordinates, and camera settings, to ensure they are accurate and consistent across the dataset
  • Verifying that the metadata aligns with the intended flight plan
  • Looking for significant variations or inconsistencies in quality or appearance within the dataset
  • Verifying the accuracy of the data by means of comparison with ground control points or reference data with known coordinates
  • Assessing the resolution and level of detail in the data to meet client’s requirements
  • Ensuring that the GCP coordinates were measured with high accuracy to increase the data accuracy

Examples of low quality drone data and with significant artifacts or distortions

Overexposed drone image
Underexposed drone image 
Drone image with consistent shadows
Underexposed and with consistent shadows drone image
Orthomosaic map generated from drone imagery with inconsistent exposure (underexposed on the left, overexposed on the right)
(a) saturated image; (b) vignetting; (c) chromatic aberration; (d) mosaic blurring in overlap area; (e) incorrect color balancing; (f) hotspots on mosaic due to bidirectional reflectance effects; (g) relief displacement (tree lean) effects in final image mosaic; (h) image distortion due to DSM errors; (i) mosaic gaps caused by incorrect orthorectification or missing images
Blurry drone image
In the left: grainy image. In the right: good image
Orthomosaic with high accuracy (around 10 cm) showing an horizontal offset compared with satellite imagery
Orthomosaic with low accuracy (around 3 m) showing an horizontal offset compared with satellite imagery
Orthomosaic map with distortions on the borders
Orthomosaic map not properly clipped

Author

Margherita Bruscolini
Head of Drones

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