Show us your Ground Control Point targets - Share your experience

I want to revise my Ground Control Points, but need some inspiration and good ideas. Show me a photo of your GCP targets!

Here are my own findings:

  • Ability to securely fix the GCP’s to the ground increases precision (no risk of accidental movement from measurement to flight
  • use stiff plates, warping causes inprecision
  • use colors not present in the operating environment
  • using auto-detectable, auto-recognized targets almost elimates the need for manual placement
  • Put a number on it, clearly visible from the drone.
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I use black and white vinyl floor tile stuck to Tyvek with ChrisNik Mag Hub in center with metal washer. Being on Tyvek it folds up easy.


What’s is your experience with tuvek? Does it last? Easy to clean?

Tyvek is a very tough fabric used for house wrap. It will outlast the vinyl tiles for sure.

You dont have issues with imprecise photogrammetry when using curved/curly targets?

You fixate to the ground by nail in center?
What about grabbing the corners?

What is the minimum size (pixels) for automatic detection?

I tend to use much smaller targets. Below the latest example for a low costs student project in a wet peatland where the target should survive mowing. It was nailed on a piece of wood which was put 50 cm into the peat.


What I ask myself is whether the targets give meaningful information about the accuracy of the orthomosaic since they are totally different from the environment I’m mapping.

$1.50 at your local Home Depot. 60D nail to set it.



Paint bucket lid, or what is that?

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Guess that depends on your target?
Photoscan’s/Metashape’s recommends a certain size for their auto-recognizable targets.

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Yes, 5-gallon bucket lid.


I could not finde any pixel size information in the Metashape documentation. Are there any experience values?
I only habe the experience that white paper is a problem because the reflection outshines the black marks on a DIN A4 paper.

I was using a black and white vinyl, which I was lucky to find in the local store. 25*25 cm square. Total size of GCP is 50 cm to 50 cm. Works good.

On the glaciers we use special GCPs, that are made out of fabric. They are washable and have quite durable coating against water and dirt. Colours are orange and blue. On a glacier everything is black-and-white, so such colours are easier to spot on the images. We added grommets on the corners and in the center of GCP. By using screws or just nails we can fix it in place and the grommet in the middle is easy to measure with the pole.

Hm, it seems they have taken the numbers out for the v1.5 documentation. In the older documentation it said that the inside circle should be no more than 20 px in diameter.
In the new docuementation they have also added a few disclaimers, namely that the targets will be sort of big, if they should be detectable at say 10 cm/px GSD.

What accuracy do you usually get out of that, when processed by photogrammetry?

What do you mean by accuracy?

My RMSE’s are usually around 0.5 - 1.0cm with the orange lids. I use to use orange/blue quads with the same accuracies.

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Your RMSE when processed.
I was told that any other than flat surfaces for GCP would be more likely to introduce additional errors.
Would be nice with some numbers. Then I wouldn’t have carry around heavy boards :smiley:

I don’t know for now, because still haven’t processed any model with those GCPs. You think it will differ from black-and-white? I am checking the position of the marker on every picture and correcting if it is erroneous.

I think color is irrelevant, especially if the GCP marker placement is done manually in the software.

What I would worry about would be the curvature or or unevenness of a GCP. Not sure how well photogrammetry software copes with such changes. If it averages it for the GCP, that could give an error of several cm on the Z axis.

Since photogrammetry software doesn’t know, how a GCP looks like, I think it just treats them as all other objects on the picture. Unless you are using in-built marker recognition, which I don’t use.