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

IF 1.8 Q2 GEOGRAPHY
J. Guth, S. Keller, S. Hinz, S. Winter
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引用次数: 2

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 often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification 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 for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.
面向众包路网数据库中道路类误差的检测、表征和评定
OpenStreetMap (OSM)凭借其全球覆盖和开放数据库许可证,最近获得了广泛的欢迎。它的质量足以满足许多应用程序,但由于它是众包的,错误仍然是一个问题。例如,路网相关标签中的错误会影响路由应用程序。特别是道路分类错误经常导致对容量、最大速度或道路质量的错误假设,可能导致路由应用程序绕路。这项研究旨在自动发现潜在的分类错误,然后由人类专家进行检查和纠正。我们开发了一种新的方法来检测OSM中的道路分类错误,该方法通过搜索分层道路网络中不同层次的不连接部分和间隙来检测道路分类错误。确定了不同的参数,表明道路网络的差距。然后将这些参数组合到一个评级系统中,以获得错误概率,从而向人类用户建议可能的错误分类。该方法适用于澳大利亚新南威尔士州的一个范例案例。结果表明:(1)在间隙处发现的分类错误比在未连接部分发现的分类错误更多;(2)间隙搜索使用户能够使用开发的表示错误概率的评级系统快速发现分类错误。在未来的工作中,该方法可以扩展到包括OSM中用于评级系统的可用标签。实现的源代码可通过GitHub获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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