If the outputs have overlaps, you’re dealing with a multi-label classification use case, i.e. each pixel might belong to more than one class?
If so, you could still use a single segmentation map and just return multi-label predictions.
I’m still not understanding, how Keras deals with this problem.
For example, if one pixel belongs to class1 and class2 at the same time. You would force your model to learn it as class1 in one iteration and class2 in another one?
Would your model be able to predict more than one class for each pixel then?
I’m digging a bit deeper, as your use case is most likely possible to implement, I just have my doubts about the correctness.