There's a PhD in this
Asides from the space junk, there are some wonderfully intersting problems in the collaborative data space. Enough for a good PhD even.
For instance: 1) Two people map the trees in their local park. One person did it several years ago and paid great attention to accuracy and detail. Some years later a second person maps the same park, while their map takes into account all the recent changes, the collected data was rushed and is not as accurate. 2) I use my GPS to map dangerous rocks in a local bay, what can we say about the absence of dangerous rock markers elsewhere in the bay?
A lot of this is covered in established theory (like sins of ommission, sins of commission), but collaborative data adds some nice complications and scale problems. My own view is that with enough data and understanding of biases in data sources, we can average out the real answer.