https://github.com/rrwen/poster-gisci-osmol

Conference poster and short paper titled "Outlier Detection in OpenStreetMap Data using the RandomForest Algorithm and Variable Contributions" for the GIScience Conference in 2016
https://github.com/rrwen/poster-gisci-osmol

2016 algorithm conference contribution data detection forest gis giscience learn machine open openstreetmap osm outlier paper poster random short variable

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Conference poster and short paper titled "Outlier Detection in OpenStreetMap Data using the RandomForest Algorithm and Variable Contributions" for the GIScience Conference in 2016

• Host: GitHub
• URL: https://github.com/rrwen/poster-gisci-osmol
• Owner: rrwen
• License: gpl-3.0
• Created: 2016-09-23T20:18:57.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2017-04-25T08:28:39.000Z (over 8 years ago)
• Last Synced: 2025-02-08T21:11:19.562Z (8 months ago)
• Topics: 2016, algorithm, conference, contribution, data, detection, forest, gis, giscience, learn, machine, open, openstreetmap, osm, outlier, paper, poster, random, short, variable
• Homepage: https://rrwen.github.io/poster-gisci-osmol
• Size: 3.8 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Outlier Detection in OpenStreetMap Data using the RandomForest Algorithm and Variable Contributions

  

Richard Wen, Claus Rinner  

rwen@ryerson.ca, crinner@ryerson.ca    

  

A [short peer-reviewed paper](http://giscience.geog.mcgill.ca/?page_id=33) and poster for the [Ninth International Conference on Geographic Information Science](http://giscience.geog.mcgill.ca) in Montreal, Canada from September 27, 2016 to September 30, 2016.

* [Short Paper PDF](https://github.com/rrwen/poster-gisci-osmol/blob/master/paper.pdf)

* [Poster PDF for Print](https://github.com/rrwen/poster-gisci-osmol/blob/master/poster.pdf)

* [Poster Website](https://rrwen.github.io/poster-gisci-osmol)

## Abstract

OpenStreetMap (OSM) data consists of digitized geographic objects with semantic tags assigned by volunteer contributors. These human and machine readable tags are edited manually and automatically to improve data quality. The structure of the tags allow machine learning algorithms to support user editing by learning to identify irregular objects and data patterns. This research experimented with a random forest algorithm on geospatial variables for geospatial outlier detection and knowledge discovery in OSM data without ground-truth reference data.

## Acknowledgements

We would like to thank the Geothink Social Sciences and Humanities Research Council (SSHRC) Partnership Grant for the funding provided during the duration of this research. Map data copyrighted OpenStreetMap contributors and available from http://www.openstreetmap.org

## Developer Notes

The code used in this short paper was developed for a Masters thesis available at [github.com/rrwen/msa-thesis](https://github.com/rrwen/msa-thesis)