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osm-changeset-classification

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Classify OpenStreetMap changesets for detecting spam, imports, reverts, and mapping errors.

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# OpenStreetMap Changeset Classifier For Detecting SPAM, Imports, Reverts and Mapping Errors

This repository is deep learning a classifier for OpenStreetMap changesets. It is used to
detect SPAM, tagging errors, imports, and imports with tagging issues in the OpenStreetMap database.

## Tools

createhistorydatabase.py - Creates a custom sqlite history database from the raw history and changeset download files. The custom 
history database is not needed unless you plan on adding many new changesets to the database. The repository has 26,000 
changesets already downloaded and checked in. It takes about 4 days to create, and
uses over 600 GB of disk space on a normal desktop system. 
  
updatechangesetcache.py insures that the trainingdata/cache directory has all of the changesets listed in changeset.csv. 
Any changeset listed in changeset.csv, but not downloaded into the trainingdata/cache, will be extracted from the 
custom sqlite history database or downloaded from the osm api if the history db is not setup.
This program only needs to be run when new changesets are added to changeset.csv

train.py trains the classifier. The classifier is heavily based on the [Keras newsgroup text classification sample application](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html). Instead of using the news group data, it instead uses the OSM changeset tags pulled from the trainingdata/cache directory.

classify.py runs the trained classifier. If no parameters are supplied it will run the classifier over the training data and report misclassified changeset to 
standard out and to toreview.txt. If you supply a changeset number on the command line, it will download the changeset and classify it. For example, python classify.py 5071219, will classify changeset 5071219.

reviewtrainingdata.py is an interactive terminal program that prompts the user to classify un un-validated changesets. The misclassified files 
reported by classify.py are written to a file called toreview.txt, and if toreview.txt is supplied on the command line, the misclassified changeset ids 
will be reviewed rather than the un validated changesets.

In the osmclassify/ directory has a ChangeSet class for downloading, loading, saving the cached changeset files in trainingdata/cached. The files in trainingdata/cache are
a custom XML format streamlined for this project that are not standard OSM files.

## Data
The trainingdata/ directory contains the training data.

The trainingdata/changeset.csv is a CSV file where columns are:
- changeset id - The OSM Changeset ID.
- From/Note - Where the row came from.
- Validated - Y,N if the changeset have been manually validated with reviewtrainingdata.py
- SPAM - Y,N if the changeset is SPAMy.
- Import - Y,N - The changeset is an import or automated edit.
- Tagging Error - Y,N - If the changeset added an invalid tag to OSM.

The trainingdata/cache directory contains the actual 
contents of the changesets listed in trainingdata/changeset.csv. The cached changeset
files are custom XML files, that have the changeset comments, and a diff of
the tags modified by the changeset. These files are checked in so downloading
and processing the raw OSM data is not required.

The trainingdata/changeset.csv file was seeded from many sources. 
- Frederik Ramm posted a csv file to the talk-us list in Feb-2018 containing nodes that he suspected of being SPAM.
- Random changesets 
- Using regular expressions of the changeset dump, changesets that are reverting another changesets. 
- Using regular expressions of the changeset dump, changesets that are mentioned in the command of a revert changeset as reverted.
- Running intermediate versions of the classifier over millions of changesets from 2017.

## Known Dependencies

1. The GloVe embedding here http://nlp.stanford.edu/data/glove.6B.zip
(source page: http://nlp.stanford.edu/projects/glove/), and must be installed 
into a glove.6B directory that is not checked in because it is too large.
2. Keras 
3. Python 3.x
4. A GPU is not required.

## Collaborators are Wanted! 
- The most important part of this repository is the the trainingdata/changeset.csv file. If you are OSM'er that is passionate about keeping SPAM out of OSM, please send 
me new SPAM changesets via the github issue tracker or via a pull request. Simply giving me the changeset id and saying it is SPAM is enough. 
- This classifier by itself is not much good unless it is integrated into
actual tools that the OpenStreetMap community can use.

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