Ortho Quality Metrics


I am trying to find the best way to quantitatively analyze the quality of the orthos I am generating. Visual inspection is of course first order of business ( here is a good place to say kudos again to the awesome (!) curtain comparison tool https://opendronemap.github.io/UAVArena/ by @pierotofy and everyone else that contributed ).

But I am looking for a more quantitatively rigorous method /tools to compare orthomosaics after generation by different engines. For instance defining and measuring noise metrics, resolution, accuracy, geo-referencing etc.

Happy to hear suggestions.



Per the above, the opensfm/reports folder contains files called tracks.json and reconstruction.json which have information on “wall times” and many other params.

Where can I get information on what those are?


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Found this


After sifting through some of the output, the below maybe sort of KPI:

In odm 1.0.0, the new shots.geojson file in the odm_report output folder should hold all the coordinates of all images. I have seen runs that completed successfully but the output was garbled, where this number was very low compared to the known number of input images. (say only 2 out of 98 or so)

You might want to check out the last thread on quality reporting that I’m aware of. I imagine it would be a valuable addition and drive further interest for the photogrammetry users…would you be interested in starting a fund?

…I found outside of PIX4D’s quality report many apps tend to offer little in the quantitative metrics. I use GIS on the regular and with image analysis tools one could run a number of unsupervised image classification processes and then quantitatively compare the raster outputs in a number of ways. Relative and or absolute accuracy? I use NAIP imagery for georeferencing but it can still be dubious if you don’t have well distributed reference points (there’s still a bit of art to georef). However, I would start by asking what is the specific question are you trying to answer?



Thanks Nick!
Appreciate the reply and pointers. The main questions is a whether there is an established effective quantitative metric or metrics to judge an ortho aside from simply looking at it. Specifically, if I am looking for the best set of params, I would like to be able and run some routine to filter out the first order of orthos that don’t cut it. In the end I may be left with a few that I will have to use my eye-balls for. But with not too many params, the permutations are many and I’d like to make more efficient.
Per classification - I think it might get shady. My data can become fairly uniform - field like - so I don’t know how robust this path may be.
Per pix4d - their report is indeed helpful so I tend to go along that route for ODM

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