Machine Learning
Road analysis based on texture similarity evaluation
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
OpenStreetMap: User-Generated Street Maps
IEEE Pervasive Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
Biketastic: sensing and mapping for better biking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatic Road Environment Classification
IEEE Transactions on Intelligent Transportation Systems
An introduction to random forests for multi-class object detection
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Improving geo-spatial linked data with the wisdom of the crowds
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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In this paper, we describe a multi-modal bike sensing setup for automatic geo-annotation of terrain types using web-based data enrichment. The proposed road/terrain classification system is mainly based on the analysis of volunteered geographic information gathered by bikers. By using participatory accelerometer and GPS sensor data collected from cyclists' smartphones, which is enriched with data from geographic web services, the proposed system is able to distinguish between 6 different terrain types. For the classification of the web-based enriched sensor data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies every instance of road (over a 5 seconds interval) and maps the results onto the user collected GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types and create more advanced route statistics. The accuracy of the bike sensing system is 92% for 6-class terrain classification and 97% for 2-class on-road/off-road classification.