Hi Peda,
yes indeed, yours is a very pertinent question. For the problem of
differentiating between low-detail complex buildings and high-detail
simple buildings currently I have two ideas. One, check the spatial
distribution of the level of detail. This is based on the assumption
that buildin
Hi,
There is obviously plenty of data that represents "good" changes. The
data working group reversions could be used to train a classifier on
what a bad edit looks like. After that, looking for change sets that
are logically erased after a short period of time (say 2 weeks), might
also yield some
Hi Animesh,
You can check out the vandalism page on the OSM Wiki that provides a pretty
good overview about OSM vandalism and the challenge of detecting it [0].
I think a binary classification won't be as straightforward for creating a wide
sweeping 'vandal detection' tool because the problem
3 matches
Mail list logo
