25/11/2013 A method to automatically identify road centerlines from georeferenced smartphone data XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013) George H. R. Costa, Fabiano Baldo {dcc6ghrc,

2 Agenda  Introduction  Objective  Related work  Proposed method  Tests and Results  Conclusion and Future work

3 Introduction  Digital road maps have gained fundamental role in population’s daily life  Navigation systems etc.  It is essential that maps reflect reality as well as possible  Generated from accurate data;  Periodic updates.  Possible source of data: GPS traces

4 Introduction  By combining many traces it is possible to generate maps  Example: OpenStreetMap  Users use uploaded traces to create/update maps  However, all map editing is done manually  Automatic solutions would be more effective  Could allow maps to be updated faster  Feasible: [Brüntrup et.al. 2005] and [Cao and Krumm 2009] also support this idea

Challenges  How to obtain the data needed to generate maps?  Smartphones  Contain many sensors, including a GPS receiver  Represent half of the Brazilian cellphone market [GFK 2013] 5 Source: Garmin

Challenges  To create road maps it is necessary to find the roads’ centerlines  How to analyze the traces to identify road centerlines?  Approximated result  Evolutive algorithm 6 Source: author

7 Objective  Therefore, the objective of this work is to: Propose a method to identify road centerlines using an evolutive algorithm in order to generate and update road maps

8 Related work  Characteristics gathered from other works:  Independence from initial maps [Brüntrup et.al. 2005; Cao and Krumm 2009; Jang et.al ]  Usage of heuristics to remove noise from the traces [Brüntrup et.al. 2005; Cao and Krumm 2009; Zhang et.al. 2010; Niu et.al. 2011]  Characteristic introduced by this work:  Traces’ date of recording is taken into account to generate up-to-date maps

Data source 9 Source: author

Preprocessing  Reduces noise; saves all traces to database 10 Source: author

11 Road centerlines 1.Query database to get all traces ordered by date and accuracy i.Most recent first ii.Most accurate first Source: author

12 Road centerlines Source: author

13 Road centerlines Source: author

14 Road centerlines

15 Road centerlines

16 Road centerlines

17 Road centerlines  Recent traces: weight closer to 1  Older traces: weight closer to 0 Influence of Time

18 Road centerlines Influence of Accuracy Weight Accuracy

19 Road centerlines Influence of Distance Weight Distance

20 Road centerlines Source: author Closer to highest concentration of points: smallest overall distance Closer to points high better accuracy

21 Road centerlines 3.Evolutive algorithm  60 generations  20 candidate solutions per generation  Elitism: 2 best candidate solutions are preserved to the next generation Source: author Evolutive algorithm loop:

22 Road centerlines Source: author

23 Results  Implemented in Python  DB: PostgreSQL + PostGIS  Data collected between 27/01/2013 e 15/06/2013  4237 traces  points

24 Results  Tests: comparison between  Proposed method’s results  Satellite images  Google Earth  Executed on places with complex road structures

25 Tests (1) Roads intersect Source: Google Earth / author Satellite image

26 Tests (1) Points collected (filtered) Source: Google Earth / author

27 Tests (1) Way centerline Direction of movement differentiates traces It is possible to improve filtering... Final result Source: Google Earth / author

28 Tests (2) Roads with different direction of movement Roads with same direction of movement Satellite image Source: Google Earth / author

29 Tests (2) Points collected (filtered) Source: Google Earth / author

30 Tests (2) It is possible to improve filtering... Direction of movement differentiates traces Final result It is possible to improve parameters... Source: Google Earth / author

31 Results  Small difference between the satellite images and the method’s results  Average distance (100 points): 2.95 meters  Cannot affirm which one is more accurate  Certain questions cannot be controlled  Ex.: satellite images might be somewhat out of position

32 Conclusion  Different from similar methods because:  Takes into consideration the influence of the traces’ date of recording;  Collects data using smartphones;  Finds centerlines using evolutive algorithm.  Tests showed little difference to satellite images  It is still possible to optimize parameters to achieve better results

33 Future work

34 Bibliografia  Brüntrup, R. et. al. (2005) “Incremental map generation with GPS traces”. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems.  Cao, L. e Krumm, J. (2009) “From GPS traces to a routable road map”. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, EUA: ACM Press.  Garmin (2010) “Garmin-Asus smartphones reach new markets”. (accessed on Nov 22).  GFK (2013) “GfK TEMAX BRASIL T2 2013: Crescimento no mercado com forte influência de materiais de escritório e periféricos”. (accessed on Nov 18).  Jang, S., Kim, T. e Lee, E. (2010) “Map Generation System with Lightweight GPS Trace Data”. In: International Conference on Advanced Communication Technology.  Niu, Z., Li, S. e Pousaeid, N. (2011) “Road extraction using smart phones GPS”. In: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications. New York, EUA: ACM Press.  Zhang, L., Thiemann, F., Sester, M. (2010) “Integration of GPS traces with road map”. In: Proceedings of the 2nd International Workshop On Computational Transportation Science. San Jose, EUA. ACM Press.

25/11/2013 A method to automatically identify road centerlines from georeferenced smartphone data XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013) George H. R. Costa, Fabiano Baldo {dcc6ghrc,