Intelligent Prioritization and Filtering of Labels in Navigation Maps

Abstract

The description of objects in navigation maps by textual annotations provides a powerful means for orientation and visual data exploration. However, displaying labels for all features leads to a cluttered map with unreadable labels and occluded information. Therefore, the overall goal is to display the most important and filter out the less important labels. In this paper, we present a general approach for filtering labels. We use the navigation in automotive maps as an application to test our approach. This involves the creation of a priority metric for ranking labels in maps. Our flexible system allows runtime configuration of the priority. Moreover, we keep the temporal coherency of label filtering; hence, jittering of labels does not occur. The system is predictable, modular, and can easily be adapted to new applications. On medium-class hardware, our real-time system is capable of filtering on average 1000 labels within 12 ms. A concluding expert study validates our approach for navigation purposes. All candidates approve the resulting clear labeling layout.