Outlier Detection in OpenStreetMap Data using the Random Forest Algorithm

What:
Poster
When:
Thursday Sep 29   03:30 PM to 05:30 PM (2 hours)
Tags:
OpenStreetMapgeospatial propertiesmachine learning
Discussion:
0
OpenStreetMap (OSM) data consist of digitized geographic objects with semantic tags assigned by the volunteer contributors. The tags describe the geographic objects in a way that is understandable by both humans and computers. The variability in contributor behaviour creates reliability concerns for the tagging quality of OSM data. The detection of irregular contributions may improve OSM data quality and editing tools. This research applies the random forest algorithm on geospatial variables in order to detect outliers without ground-truth reference data to direct human inspection. An application to OSM data for Toronto, Ontario, was effective in revealing abnormal amenity tagging of school and hospital objects.
Participant
Ryerson University
Participant
Ryerson University

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