Push-based Spatial Crowdsourcing for Enriching Semantic Tags in OpenStreetMap

Abstract

OpenStreetMap (OSM) is a popular community-driven mapping platform with voluntary contributions from (amateur) cartographers. However, it is a difficult process for the cartographer to identify the areas where she can best contribute to OSM. Furthermore, the current OSM spatial entities are missing many tags; for example, top three road network tags, Name, Source, and Surface, are available only for the 10% of the total road segments. Our paper aims to improve the quantity and quality of the road network tags by actively pushing the nearest road segments for the cartographer to be mapped. We propose a push-based spatial crowdsourcing method to achieve this objective, and validate it by focusing on road segments in OSM. Specifically, we formally define the batchbased maximum road segment task assignment problem and suggest methods based on heuristics like travel distance and road segment task grouping. Finally, our experimental evaluation verify the applicability of our assignment solutions by comparing the resulting number of assigned tasks. With regard to the number of assigned road segments, our junctions-based and road segment-based heuristic methods, outperform the baseline methods by five and two times, respectively.