Automatic Geographic Enrichment by Multi-modal Bike Sensing

Abstract

AbstractThis paper focuses on the automatic geo-annotation of road/terrain types by collaborative bike sensing. The proposed terrain classification system is mainly based on the analysis of volunteered geographic information gathered by cyclists. By using participatory accelerometer and GPS sensor data collected from the cyclists’ smartphones, which is enriched with image data from geographic web services or the smartphone camera, the proposed system is able to distinguish between 6 different terrain types. For the classification of the multi-modal bike data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies the features of every instance of road (over a 5 seconds interval) and maps the results onto the corresponding GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types, create more advanced route statistics and facilitate geo-based recommender systems. The accuracy of the bike sensing system is 92 % for 6-class terrain classification. 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.KeywordsMulti-modal sensingImage classificationGeo-annotationBike-sensingVolunteered geographic informationAccelerometer analysisMobile visionMachine learning