CrowdGIS: Updating Digital Maps via Mobile Crowdsensing

Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose CrowdGIS, an automatic store self-updating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user's shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy. Note to Practitioners - This paper was motivated by the problem of automatically updating digital maps in a manner of mobile crowdsensing. Existing approaches can update stores in maps through a manual survey or update roads automatically from mobile crowdsensing data. Since the store information is a crucial component in digital map, this paper suggests a novel approach to automatically update stores in digital maps through mobile crowdsensing. This is necessary, in general, because the accuracy of digital map will directly affect the quality of various location-based services. Therefore, the system proposed in this paper is useful for engineers and developers to obtain precise digital maps for localization, navigation, automatic drive, etc.