Hi, the notebook is slightly updated w.r.t. the current version of sentinelhub-py. For this particular line try from sentinelhub import BBox, CRS (without the common submodule).
You can find the list of package version here
If you wish to install an older version you can use pip install sentinelhub==2.3.0 however we cannot guarantee that the old versions still work with the current SentinelHub service
This line tries to import from the time_lapse.py file, which is located next to the notebook in the repository. If your setup doesn’t detect it as an importable module (it doesn’t detect the file while searching for modules) then it cannot import it.
We don’t usually test our notebooks for Google Colab
In general the repository hasn’t been updated for 4 years, so you are sure to encounter further issues. Is this notebook of particular interest to you?
I’m trying to setup an automated workflow to do time-lapse image sequences bypassing the EO Browser web tool which is limiting for some of my reasearches.
Basically I just need to extract hires image sequences of time-series with specific custom scripts.
Working within Colab environment is pretty handy for me so I was looking at notebooks.
Do you think that there’s another process/method or resources I can dig to achieve this?
Perhaps take a look at the process API example on the sentinelhub-py repository. Example 8 (the last one) shows how to download and display multi-time images of an area. That one has no additional requirements aside from the sentinelhub package, so I have high hopes that it works in Colab as well.
I think it’s easiest if you go through the example of the process API, and if it’s not sufficient we can help you further. If you’d like some ‘helpers’ for the process you could also use eo-learn (here is the example for downloading SentinelHub data), but I guess that adds another layer of complexity.
The notebook link is indeed broken, as the file was moved here, I’ll get it corrected.
If you’re after images of very large areas then the batch processing approach is indeed the right one for you, but it is not explained in the above notebook. The best example of Batch Process API would be the one on the sentinelhub-py repository, but batch processing is a bit more complex.