1: https://drive.google.com/file/d/1Fw5SdZ-aAiHrZERnIkVgll_OFnt...
A challenge of open source software is that it largely only gets created when the technical complexity and expertise barrier is low enough. Above that threshold, the small number of people capable of contributing combined with the large number of man-hours required for a correct implementation is beyond the resources typically available to such a project. Geospatial is a good example of this pattern (database engines are another).
The core of them mostly do the thing they claim to do, but if you feed them data that's just slightly off, then it'll break in interesting ways.
Speaking based on a lot of experience[1]. Nothing in the geospatial or environmental data world is easy and there will always be an unexpected problem lurking right behind the corner, regardless of how many of them you've already fixed or worked around.
Still, the complexity of the problem space also makes it fascinating. As an example, the definition and measurement of something that's seemingly very simple: mean seal level is actually tremendously complicated if you think about it. The Wikipedia article talks about averaging over decades of measurements to get it right for example.
[1] I'm a part of the team behind https://data.planetos.com/datasets