@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
}

@article{10.1371/journal.pone.0019467,
  abstract = {        <p>Twitter is a free social networking and micro-blogging service that enables its                     millions of users to send and read each other's “tweets,” or                     short, 140-character messages. The service has more than 190 million registered                     users and processes about 55 million tweets per day. Useful information about                     news and geopolitical events lies embedded in the Twitter stream, which                     embodies, in the aggregate, Twitter users' perspectives and reactions to                     current events. By virtue of sheer volume, content embedded in the Twitter                     stream may be useful for tracking or even forecasting behavior if it can be                     extracted in an efficient manner. In this study, we examine the use of                     information embedded in the Twitter stream to (1) track rapidly-evolving public                     sentiment with respect to H1N1 or swine flu, and (2) track and measure actual                     disease activity. We also show that Twitter can be used as a measure of public                     interest or concern about health-related events. Our results show that estimates                     of influenza-like illness derived from Twitter chatter accurately track reported                     disease levels.</p>       },
  author = {Signorini, Alessio and Segre, Alberto Maria and Polgreen, Philip M.},
  doi = {10.1371/journal.pone.0019467},
  interhash = {56b199b8f3a3d085aef43e25b2aca06b},
  intrahash = {6c3bc3dabf1b1d0095774e87b14d3ad2},
  journal = {PLoS ONE},
  month = {05},
  number = 5,
  pages = {e19467},
  publisher = {Public Library of Science},
  title = {The Use of Twitter to Track Levels of Disease Activity and Public                     Concern in the U.S. during the Influenza A H1N1 Pandemic},
  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0019467},
  volume = 6,
  year = 2011
}