Guarding OpenStreetMap from harmful edits using machine learning
Jupyter Notebook
0
0
MIT License
Gabbar is an experimental machine learning tool designed to detect and flag potentially harmful or suspicious edits in OpenStreetMap data. It uses a pre-trained binary classifier to analyze changesets and features, providing predictions on whether edits are problematic. The project is aimed at OpenStreetMap contributors and maintainers who want to monitor data quality and integrity, offering a way to identify anomalies through its Python package and command-line interface.
Total donated
Undistributed
Share with your subscribers:
Recipients
How the donated funds are distributed
Support the dependencies of classicvalues/gabbar
Compare a features new and old versions in GeoJSON
a JavaScript library for performing geospatial operations with GeoJSON
A mature CSV toolset with simple api, full of options and tested against large datasets.
Evaluate asynchronous tasks with configurable concurrency.
parse argument options
Recursively mkdir, like `mkdir -p`
Parse, validate, manipulate, and display dates
An npm package to use the List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words
Convert JSONs returned by osm-adiff-parser to proper geojsons
Simplified HTTP request client.
Simple Statistics
JavaScript's functional programming helper library.
A strict ESLint config for my JavaScript projects
tap-producing test harness for node and browsers
Support the repos that depend on this repository
Top contributors
bkowshik
38 contributions
sgillies
20 contributions
pratikyadav
3 contributions
classicvalues
2 contributions
kapadia
1 contributions
joneskoo
1 contributions
regisb
1 contributions
Recent events
Kivach works on the Obyte network, and therefore you can track all donations.
No events yet