In relation to the large dataset that you are processing - the corridor projects are more prone to inaccuracies. For those projects, the accurate geolocation of the images, as well as the efficient overlap, is very important. It prevents from many issues such as multiple blocks or big errors on the edges that also occurs in your project. If there is a weak area (e.g. low overlap, low image content etc.), the error will increase as diverging from the breakpoint. Sometimes even GCPs may not manage to adjust the project correctly.
Your project consists of 872 images. Twenty-eight hours to compute only the Tie points is way too much. Could you share with us your computer specification - CPU, RAM, GPU so we could have a closer look at your hardware attempting to speed up the processing. Geolocation of the images always makes the process go faster, but from my understanding, you used already that information, am I right?
At this point, I need to ask for a workflow explanation. I understand that you used your Mavic 2 Pro having very good GPS signal during acquisition. However, I have a lack of information on:
- How many flights did you have? Three?
- Did you decided to process all the images together or you split the dataset per subprojects and merged them together?
- Does the project on the cloud contain already your custom processing options?
- Did you acquire any GCPs for this particular project? I understand that in such a wild area it’s very hard to collect some GCPs. However, this information would significantly help us with this project.
I look forward to your response so I could give you some processing advices.