@inproceedings{4587819,
  abstract = {We present a statistical learning approach for finding recreational trails in aerial images. While the problem of recognizing relatively straight and well defined roadways in digital images has been well studied in the literature, the more difficult problem of extracting trails has received no attention. However, trails and rough roads are less likely to be adequately mapped, and change more rapidly over time. Automated tools for finding trails will be useful to cartographers, recreational users and governments. In addition, the methods developed here are applicable to the more general problem of finding linear structure. Our approach combines local estimates for image pixel trail probabilities with the global constraint that such pixels must link together to form a path. For the local part, we present results using three classification techniques. To construct a global solution (a trail) from these probabilities, we propose a global cost function that includes both global probability and path length. We show that the addition of a length term significantly improves trail finding ability. However, computing the optimal trail becomes intractable as known dynamic programming methods do not apply. Thus we describe a new splitting heuristic based on Dijkstra's algorithm. We then further improve upon the results with a trail sampling scheme. We test our approach on 500 challenging images along the 2500 mile continental divide mountain bike trail, where assumptions prevalent in the road literature are violated.},
  author = {Morris, S. and Barnard, K.},
  booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
  doi = {10.1109/CVPR.2008.4587819},
  interhash = {4886e90202778766c8832765ff291e44},
  intrahash = {bcec19c981718168825ca203b6935204},
  issn = {1063-6919},
  month = {June},
  pages = {1-8},
  title = {Finding trails},
  url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=4587819},
  year = 2008
}

@inproceedings{Stenneth:2011:TMD:2093973.2093982,
  abstract = {The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.},
  acmid = {2093982},
  address = {New York, NY, USA},
  author = {Stenneth, Leon and Wolfson, Ouri and Yu, Philip S. and Xu, Bo},
  booktitle = {Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
  doi = {10.1145/2093973.2093982},
  interhash = {07950385ca6bb9138db4f20bb3dd7698},
  intrahash = {6eff579bee29983fbf72403faa9b04ae},
  isbn = {978-1-4503-1031-4},
  location = {Chicago, Illinois},
  numpages = {10},
  pages = {54--63},
  publisher = {ACM},
  series = {GIS '11},
  title = {Transportation Mode Detection Using Mobile Phones and GIS Information},
  url = {http://doi.acm.org/10.1145/2093973.2093982},
  year = 2011
}