Detecting inconsistent information in crowd-sourced street networks based on parallel carriageways identification and the rule of symmetry

Crowd-sourced geographic information has great potential in scientific and public domains, and is recently under consideration by geospatial professionals as an alternative to traditional spatial data collection. The success, however, implies a need to build long-term reliance on the crowd-sourcing projects, and poses growing concern over the quality of the constantly evolving data. In general, we aim to develop an approach that uses geographic rules to identify inconsistent information in street networks without relying on external sources. This paper focuses on a more challenging sub-process that aims to identifying inconsistent information using the rule of symmetry. That is, information (e.g. name, class, speed limit, etc.) in parallel carriageways (e.g. divided highways) always constrains each other. The process starts with a clustering of related streets into well-defined or ambiguous situations using a DBSCAN-inspired technique; then two pairing strategies are designed for both situations. To address the challenging problem of pairing carriageways in ambiguous situations, three pairing algorithms (stroke-based, tree-based, and mixed) are devised based on the idea of using expanded ‘receptive field’ to disentangle the ambiguities; each has a focus on efficiency, effectiveness, or their tradeoff. Evaluating the algorithms against 7 selected datasets shows that all three algorithms reached satisfactory performance (F1-score > 92%) for ambiguous situations, and much higher accuracy for the whole datasets. Then, we applied our approach to over 40 datasets worldwide and detected inconsistencies (i.e. dissimilar values in paired carriageways) in crowd-sourced and authoritative street networks. We evaluate the identified inconsistencies, analyze the possibilities of our approach in suggesting corrections to problematic data, and discuss its effectiveness, issues, and future directions. We thereby demonstrate that the proposed approach is effective for quality assurance, and can be used to assure the quality of crowd-sourced and authoritative mapping projects during their evolution without relying on ground-truth.