[nestorD]

It does look like it would be doable without neural networks. My guess (having read their paper but not worked in the domain) is that it would be doable but requires a lot of iterations, tweaking parameters and dealing with corner cases. That would give you something running almost instantaneously but it would take a year to develop with tight feedback from domain experts.

Or, you can throw a (relatively standard for that kind of task) neural network at the problem, have a prototype at the end of the week and something solid by the end of the month.

In those conditions it makes sense to go with the neural network based solution (however, I do hope that having this result will push people to work on a more traditional approach that is competitive with the neural network based one).

[mdp2021]

The chances of the problem not having being already tackled with deterministic algorithms is the thinnest - and in fact, yes, there do exist tools, as expected.

See the sections about 'hillshade' at the OpenTopoMap "from scratch" instructions, at https://github.com/der-stefan/OpenTopoMap/blob/master/mapnik...

[nestorD]

It has but I am hoping that a paper accomplishing the task with machine learning will move the goalpost (it has happened in other areas of computer vision).

[mdp2021]

> move the goalpost

I don't think I understand your expression there: do you mean that "the problem of producing relief bitmaps from elevation data could lead to new achievements in our knowledge relevant to Neural Nets"? I am not sure why.

If you mean, more consistently with your original parent post, "lead to improvements in the past traditional techniques towards the same goal" - I would say that I don't see much space for leaping better results than those already possible.

[nestorD]

I mean that results that would have previously been considered good enough for classical methods could now be considered improvable leading to renewed work into application of traditional methods to this subject.