OSM Changeset Analyser, osmcha, is a Python package to detect suspicious OSM changesets. It was designed to be used with osmcha-django, but also can be used standalone or in other projects.

pip install osmcha

You can read a replication changeset file directly from the web:

c = ChangesetList('https://planet.openstreetmap.org/replication/changesets/002/236/374.osm.gz')

or from your local filesystem.

c = ChangesetList('tests/245.osm.gz')

c.changesets will return a list containing data of all the changesets listed in the file.

You can filter the changesets passing a GeoJSON file with a polygon with your interest area to ChangesetList as the second argument.

Finally, to analyse an especific changeset, do:

ch = Analyse(changeset_id)
ch.full_analysis()

You can customize the detection rules by defining your prefered values when initializing the Analyze class. See below the default values.

ch = Analyse(changeset_id, create_threshold=200, modify_threshold=200,
  delete_threshold=30, percentage=0.7, top_threshold=1000,
  suspect_words=[...], illegal_sources=[...], excluded_words=[...])

The command line interface can be used to verify an especific changeset directly from the terminal.

Usage: osmcha <changeset_id>

osmcha works by analysing how many map features the changeset created, modified or deleted, and by verifying the presence of some suspect words in the comment, source and imagery_used fields of the changeset. Furthermore, we also consider if the software editor used allows to import data or to do mass edits. We consider powerfull editors: JOSM, Merkaartor, level0, QGIS and ArcGis.

In the Usage section, you can see how to customize some of these detection rules.

We tag a changeset as a possible import if the number of created elements is greater than 70% of the sum of elements created, modified and deleted and if it creates more than 1000 elements or 200 elements case it used one of the powerfull editors.

We consider a changeset as a mass modification if the number of modified elements is greater than 70% of the sum of elements created, modified and deleted and if it modifies more than 200 elements.

All changesets that delete more than 1000 elements are considered a mass deletion. If the changeset deletes between 200 and 1000 elements and the number of deleted elements is greater than 70% of the sum of elements created, modified and deleted it's also tagged as a mass deletion.

The suspect words are loaded from a yaml file. You can customize the words by setting another default file with a environment variable:

or pass a list of words to the Analyse class, more information on the section Customizing Detection Rules. We use a list of illegal sources to analyse the source and imagery_used fields and another more general list to examine the comment field. We have also a list of excluded words to avoid false positives.

Verify the changesets made in iD editor to check the host instance. The trusted iD instances are: OSM.org, Strava and ImproveOSM.

To run the tests on `osmcha`:

git clone https://github.com/willemarcel/osmcha.git
cd osmcha
pip install -e .[test]
py.test -v

Check CHANGELOG.RST for the version history.

GPLv3