Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases

Abstract

OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors of ten lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classifi cation errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching f..