Training a model that has both 2D and 1D features using a CNN
I do not see how each datapoint would have a different dimension (you have simply 2 extra scalars, always). Could you clarify that? 

Two solutions come to my head. 

The first one would be to incorporate the scalars into the original picture - augment the picture with it. So that you would have, for example, an extra row for each scalar and end up with a picture of size 66*64. How to do it reasonably is another question - I do not have experience with any problem exactly like this, but using simply one pixel for the value seems pretty marginal. Maybe having the whole row of pixels be the same, representing the value... Or maybe add another dimension and filling all the points in that dimension with the representation of the scalar.

Another solution would be to create an extra branch in the model for the scalar features (with their own classifier) and then merge them with another classifier. I personally have never done it before, but this article https://www.pyimagesearch.com/2019/02/04/keras-multiple-inputs-and-mixed-data/ seems to explain it quite well. At this site, https://stackoverflow.com/questions/47818968/adding-an-additional-value-to-a-convolutional-neural-network-input/47819063 you can also find another brief example of the code (and the guy is basically proposing both ways that I mentioned above :) )

I hope this gives you at least an idea. You will need to play with it a bit to choose the best alternative.