pre-printIn this paper we describe a method for predicting the subjective quality of a new mountain bike route for a particular subject based on routes previously ridden and ranked by the subject. GPS tracks of the previously ridden routes are over laid on rasterized topographic maps and topographic features are extracted in the vicinity of the routes using image processing techniques. The subject ranks each previously ridden route segment on four subjective qualities. The extracted topographic features and the subjective rankings are used as input vectors and target vectors to train a series of decision trees. The decision trees are then tested on a series of route segments not used in the decision tree training. The decision trees were able to exactly predict the subjective rankings with over 60% accuracy vs. 20% accuracy for random selection. When close matches are allowed in the prediction of subjective ranking (plus or minus one point vs. actual) the accuracy of the decision trees increased to 90% and above

Henderson, Thomas C.

Kirby, Richard

English

The University of Utah: J. Willard Marriott Digital Library

Analysis of Topographic Maps for Recreational Purposes using Decision Trees  Richard Kirby and Thomas C. Henderson School of Computing University of Utah Salt Lake City, USA richardk@cs.utah.edu and tch@cs.utah.edu    Abstract—In this paper we describe a method for predicting the subjective quality of a new mountain bike route for a particular subject based on routes previously ridden and ranked by the subject.  GPS tracks of the previously ridden routes are over laid on rasterized topographic maps and topographic features are extracted in the vicinity of the routes using image processing techniques.  The subject ranks each previously ridden route segment on four subjective qualities.  The extracted topographic features and the subjective rankings are used as input vectors and target vectors to train a series of decision trees.  The decision trees are then tested on a series of route segments not used in the decision tree training.  The decision trees were able to exactly predict the subjective rankings with over 60% accuracy vs. 20% accuracy for random selection.  When close matches are allowed in the prediction of subjective ranking (plus or minus one point vs. actual) the accuracy of the decision trees increased to 90% and above. Keywords—topographic maps; decision trees; recreation; machine learning; mountain bike; route selection; GPS I. INTRODUCTION The majority of people who enjoy outdoor recreational activities have access to portable GPS devices [1], either in their mobile phone or a stand-alone device.  GPS manufacturers and smart phone application developers use this capability to provide outdoor recreational users with means of creating traces of their routes that can be laid on top of rasterized topographic maps like MapSource [2] or integrated into online mapping services like Google Earth [3].  Several iPhone applications designed for cyclists [4, 5] extract training information like distance traveled, elevation gained and lost, average speed and top speed in an attempt to characterize routes.  However, most topographic map features (e.g., vegetation, road size, water features, isolation, etc.)  that characterize the subjective quality of a route, are not utilized.   The goal of this paper is to present a method that predicts the suitability of mountain bike routes to a particular subject based on a set of subjective rankings by that subject on previously ridden routes.  This is done by extracting topographic map features in the vicinity of a GPS created route and training decision trees [6, 7] to predict the user’s subjective rankings of the route based on the map features.  Estimated subjective rankings of new routes are then computed using the previously trained decision trees and GPS coordinates of the new route of interest to the user.   The following topographic features are used: water features, road size, contours, vegetation, and altitude variations.  The user preferences being studied are: aesthetic nature of route, difficulty of route, adventurousness of route, overall enjoyment of route.  Fig. 1 is an image of one of the map segments used to train and test the decision tree.  The route highlighted in yellow is a GPS track captured on a Garmin etrex Legend HCs handheld GPS and overlaid on a topographic map using Garmin’s MapSource software.   For more on extracting geographic features from raster maps, see [10, 11, 12]. II. METHOD  Our objective is to predict the suitability of mountain bike routes to a particular subject based on a set of subjective rankings by that subject on routes previously ridden by that subject.  The results are a personalized predictor of route suitability for that particular subject.   The primary tool we used to predict the suitability is the decision tree.  The decision tree is a well studied machine learning technique that has the advantage of being computationally low cost to create and even lower cost O(log N) to use.  However, decision trees require that the inputs (map features) and outputs (subjective preferences) be coded mathematically.  For our problem this starts with the collection of route data and subjective preference rankings.  The route data then has to be coded mathematically in a way that can be used as an input vector to a decision tree.  Our methodology follows these steps: • Capture route using GPS • Generate subjective rankings by route segment • Extract features from rasterized topographic maps • Mathematically code features as attribute vectors • Train the decision tree  • Test the decision tree  Each step of the methodology will now be described in further detail. 2013 12th International Conference on Document Analysis and Recognition1520-5363/13 $26.00 © 2013 IEEEDOI 10.1109/ICDAR.2013.224113705 Fig. 1. Sample Map Segment with GPS Route A. Route Capture Five routes, were ridden by the test subject who carried a Garmin etrex Legend HCs handheld GPS.  The GPS track was downloaded to Garmin’s MapSource software which displays a topographic map of the route with the GPS track highlighted in yellow.  The routes were then broken down into twenty route segments based on topography (each segment was had similar topographic features).  The twenty segments were then exported as image files.   B. Subjective user rankings After each ride, the subject was asked to rank each segment on four qualities: aesthetic nature, difficulty, adventurousness, overall enjoyment.  Definitions for these qualities were not provided to the subject as the decision tree is intended to be subject specific (each rider will have their own decision tree that predicts which routes that rider will enjoy).  The rankings are on a 1 to 5 scale with 1 being low and 5 being high. C. Extraction of Features from Rasterized Topographic maps  Topographic map features are encoded in color.  The colors vary within a range and overlapping features cause color variation which manifests itself as noise.  However, color based feature extraction produces usable groupings of map features [8].  The following algorithm was used to extract specific features: • For each pixel i, j in image of topographic map:         -     if pixel i, j falls within color range defined:  do nothing                     -    else  set each pixel i, j to zero  • Repeat for all pixels in image  Table I shows the color ranges used to extract each type of feature.  Figures 2 through 7 are the resulting sample maps segmented on these features.     TABLE I.  COLOR CODING OF FEATURES OF INTEREST  RGB Color Range  Red Green  Blue Large water feature  117 +/-20 182 +/-20 192 +/-20 Light duty roads  126 +/-50 126 +/-30 127 +/-30 Streams 55 +/-25 125 +/-25 192 +/-50 Highways  237+19/-50 32 +50/-32 36 +50/-36 Contour lines  184 +/-20 148 +/-20 103 +/-20 South facing slopes  84 +/-20 168 +/-20 117 +/-20 North and level slopes  150 +/-25 209 +/-25 169 +/-25   Fig. 2. Large water feature   Fig. 3. Light Duty Roads  Fig. 4. Streams   113806  Fig. 5. Contour Lines  Fig. 6. South Facing Slopes  Fig. 7. Flat and North facing slopes  D. Creating the Attribute Vectors for Decision Trees The terrain features that are of interest to mountain bike riders are typically a short distance from the route.  Route masks were created by sliding a circular spatial filter [9] with a radius of 50 pixels along the GPS track.  All pixels that fall within the filter are turned ‘on’ while all others are turned ‘off’.  The resulting binary image becomes the mask.  The algorithm used for creating the route mask is:  • Create mask equal to size of image (mxn) • Initialize each pixel i, j of mask to zero  • For each pixel i, j in image of topographic map: - If pixel i, j is on the route:  Center circular filter at i, j on mask  Set each pixel in mask that falls within circular  filter to 1 • Repeat for all pixels in image  To use the mask, the image with the feature or features of interest is logically ANDed with the mask.  This produces an image that only has the feature of interest in the vicinity of the route.  Fig. 8 shows the route mask created with the above algorithm logically ANDed with the original topographic map segment.   Once the image is masked and the feature of interest extracted, the importance of a given feature is calculated using equation 1.  I = Pf/(Pnb/d) (1) where I is the feature importance expressed in percent of  total pixels in mask, Pf is the number of pixels in the masked area of the feature of interest, Pnb is the total number of pixels in the mask area, and d is the diameter of the circular mask. The feature importance was then binned per the values in Table II and Table III.  The binned values for the 20 map segments are the input feature vectors for the decision tree. E. Coding Target Values for Decision Trees The subjective rankings are numerically encoded on a scale of 1 to 5, so they convert directly to decision tree target vectors.     Fig. 8. Route Mask Created with Circular Filter 113907TABLE II.  VALUES USE TO BIN FEATURE FOR FIRST FOUR FEATURES   Feature type  Bin Value Large Water Feature Small Roads Large Roads Streams 1 <2%   <.25% <.01% <.01% 2 2%-4% .25% - .5% .01%-.02% .01%-.02% 3 4%-6% .5% - .75% .02%-.03% .02%-.03% 4 6%-8% .75%-1.0% .03%-.04% .03%-.04% 5 >8% >1.0% >.05% >.05% TABLE III.  VALUES USE TO BIN FEATURE FOR LAST FOUR FEATURES  Feature type  Bin Value Contours Forests North facing Forests South facing Vertical gain 1 <1% <5% <5% <-500’ 2 1%-2% 5%-10% 5%-10% -500’ - 0 3 2%-3% 10%-15% 10%-15% 0-500’ 4 3%-4% 15-25% 15-25% 500’-1000’ 5 4%-5% >25% >25% >1000’  F. Decision Tree Managment The twenty route segments were randomly divided into training and testing sets.  Twenty random combinations of training and testing sets were created for each experiment.  The first experiment was performed with ten training sets and each of the resulting decision trees where tested on the ten unused data sets (testing sets).  A second experiment was performed using fifteen randomly selected data sets for training and testing was performed on the five unused data sets. Correct predictions by the decision tree were determined in two different ways.  The first method accepted a decision tree prediction as correct only if the decision tree exactly predicted the subjective ranking.  The second method accepted a decision tree prediction as correct if the decision tree predicted the subjective ranking plus or minus one ranking point. G. Statistics The split between training and testing sets was re-randomized for each of the twenty trials.  Within each trial, the decision tree was trained on 10,000 iterations. For each of the twenty trials the number of correct predictions of the subjective rankings was collected on the test data.  Mean and standard deviation for the 20 trials were calculated and confidence intervals were computed.  III. RESULTS Fig. 9 to Fig. 12 show the experimental results for the two different training and testing set splits and for the two different methodologies (exact match and close match).  Table IV shows the percent correct for each subjective category and for each experiment.  For ten training sets, ten testing sets, and exact answers the decision tree predicts the subjective ranking correctly between 46% and 52% of the time.  For fifteen training sets, five testing sets, and exact answers the predicted correct response increases to 61% to 63%.      Aesthetic Difficulty Adventure Overall0102030405060708090100CategoryPercent CorrectResults on Test Data - 10 training 10 testing sets  DT ErrorLower 90% confidenceUpper 90% confidenceLower 99% confidenceUpper 99% confidence Fig. 9. Results with 10 Training Sets, 10 Testing Sets, Exact Match  Aesthetics Difficulty Adventure Overall0102030405060708090100Target TypesPercent CorrectResults on Test Data - 15 training 5 testing sets  DT ErrorLower 90% confidenceUpper 90% confidenceLower 99% confidenceUpper 99% confidence Fig. 10. Results with 15 Training, 5 Testing Sets, Exact Match  Asthetic Difficulty Adventure Overall0102030405060708090100Target TypesPercent CorrectResults on Test Data - 10 training, 10 testing sets, close  DT ErrorLower 90% confidenceUpper 90% confidenceLower 99% confidenceUpper 99% confidence Fig. 11. Results with 10 Training sets, 10 Testing Sets, Approximate Match   114008Asthetic Difficulty Adventure Overall50556065707580859095100Target TypesPercent CorrectResults on Test Data - 15 training, 5 testing sets, close  DT ErrorLower 90% confidenceUpper 90% confidenceLower 99% confidenceUpper 99% confidence Fig. 12. Results with 15 Training sets, 5 Testing Sets, Approximate Match   However, for fifteen training sets and five testing sets, for each subjective ranking category, at least one of the combinations of training and testing sets produce a decision tree capable of predicting 100% of the subjective rankings correctly.  This suggests that a wider range of training examples or more training examples could produce a decision tree that is better ability to predict subjective rankings.    If close answers are allowed, using ten training sets and ten testing sets the results range from 85% to 95% depending on the subjective ranking category.  The results range from 90% to 98% accurate.    IV. CONCLUSIONS AND FUTURE WORK The results show that with ten training samples the DT was able to learn well enough to predict how the user would exactly rank routes based on topographic features 46% to 52% of the time.  This compares well to the results of a random selection of one of the five ranking categories of 20%.  Increasing the number of training samples to fifteen improved the decision trees ability to select exactly correct rankings to over 60%. Additionally, the results show that with the correct split of training and testing samples, the decision tree is able to predict 100% of the rankings correct. The above two results suggest that increasing the number of training samples improves the accuracy of the prediction and that with enough training samples, this technique could be a good predictor of the exact subjective quality of a new route for a given subject.  When close matches were allowed (predicted subjective ranking plus or minus one point from expected subjective ranking), the performance of the decision tree improved to 90% or greater with 15 training and 5 testing sets, with the aesthetic ranking being estimated 98% correctly.   TABLE IV.  PERCENT CORRECT BY SUBJECTIVE CATEGORY  Experiment (training sets/testing sets/method)  10/10/exact 10/10/close 15/5/exact 15/5/close Aesthetic 50.0% 83.0% 61.0% 98.0% Difficulty 52.0% 83.0% 62.0% 92.0% Adventure 46.0% 83.0% 63.0% 92.0% Overall 46.0% 76.5% 61.0% 90.0%  While our research involved a single rider and a relatively small number of route segments, the results are promising.  Additional areas for future research include:  Involving a wider range of subjects.  Determining if the decision tree is better at predicting the subjective rankings of a new route for a subject that is an experienced topographic map reader.  Determining the relationship between the number of training routes and the accuracy at predicting the subjective ratings of new routes.  Determining if the method transfers to other types of activities (e.g., skiing or hiking).  Consider using certain types of map features outside the mask, like mountains.  Extend the technique of using decision trees to predict subject preferences beyond mapping applications.  For example one could use the chemical analysis of wines as an input vector and subjective taste tests as the target vector and train a personalized decision tree to predict wines that might interest a subject based on past preferences. REFERENCES  [1] RNCOS, World GPS Market Forecast to 2013, 2011, Marketresearch.com. [2] Garmin Ltd., MapSource Version 6.12.4 – Topo U.S. 2008 data version 4.00 [3] Google Inc., Google earth, version 6.1.0.5001. [4] B.iCycle, Valley Development GmbH [5] Strava Cycling, Strava Inc. [6] Marsland, Stephen.  Machine Learning: An Algorithmic Perspective, CRC Press 2009. Chapter 6: Learning with Trees.  [7] CS 6350 University of Utah Course Website – A3 Decision Trees  [8] Henderson, T., Linton,T., Potupchik, S., and Ostanin, A. “Automatic Segmentation of Semantic Classes in Raster Map Images,” IAPR Workshop on Graphics Recognition, La Rochelle, France, July, 2009. [9] Gonzalez, R., Woods, R.  Digital Image Processing, Pearson Prentice Hall 2008, Upper Saddle River, NJ 07458, Chapter 3 Intensity Transformations and Spatial Filtering. [10] Y.Y. Chiang, “Harvesting Geographic Features from Heterogeneous Raster Maps,” PhD Dissertation, Univserity of Southern California, Los Angeles, CA, December, 2010. [11] T.C. Henderson and T. Linton, “Raster Map Image Analysis,”   International Conference on Document Analysis and Recognition, July, Barcelona, Spain, 2009. [12] T. Linton, “Semantic Feature Analysis in Raster Maps,” MS Thesis, University of Utah, Salt Lake City, Utah, August,  2009. 114109

Analysis of topographic maps for recreational purposes using decision trees

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Analysis of topographic maps for recreational purposes using decision trees

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The University of Utah: J. Willard Marriott Digital Library