Multi-modal bike sensing for automatic geo-annotation geo-annotation of road/terrain type by participatory bike-sensing

This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers´ smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform this classification task, the system employs a random decision forest (RDF), fed with a set of discriminative image and accelerometer features. For every instance of road (5 seconds), we extract these features and map the RDF result onto the GPS data of the users´ smartphone. Finally, based on all the collected instances, we can annotate geographic maps with the road/terrain types and create a visualization of the route. The accuracy of the novel multi-modal bike sensing system for the 6-class road/terrain classification task is 92%. This result outperforms both the visual and accelerometer only classification, showing that the combination of both sensors is a win-win. For the 2-class on-road/off-road classification an accuracy of 97% is achieved, almost six percent above the state-of-the-art in this domain. Since these are the individual scores (measured on a single user/bike segment), the collaborative accuracy is expected to even further improve these results.