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.
You can report issues or request new features in the the osmcha-frontend repository.
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:
export SUSPECT_WORDS=<path_to_the_file>
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 if the user has less than 5 edits or less than 5 mapping days.
Changesets created by users that has received more than one block will be flagged.
Verify the changesets created with iD editor to check the host instance. The trusted iD instances are: OSM.org, Strava, ImproveOSM, iDeditor, Hey, Mapcat and iD indoor, Softek and RapiD.
If you deploy an iD instance for an organization, please let us know so we can whitelist it.
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 for the version history.
- osmcha-django - backend and API
- osmcha-frontend - frontend of the OSMCha application
- osm-compare - library that analyse OSM features to input it to OSMCha
GPLv3
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