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25/11/2013
A method to automatically identify road centerlines fromgeoreferenced smartphone data
XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013)
George H. R. Costa, Fabiano Baldo{dcc6ghrc, baldo}@joinville.udesc.br
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Agenda Introduction Objective Related work Proposed method Tests and Results Conclusion and Future work
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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
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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 algorithm6
Source: author
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Objective Therefore, the objective of this work is to:
Propose a method to identify road centerlines using an evolutive algorithm in orderto generate and update road maps
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Related work Characteristics gathered from other works:
Independence from initial maps [Brüntrup et.al. 2005; Cao and Krumm 2009; Jang et.al. 2010]
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
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Source: author
Preprocessing Reduces noise; saves all traces to database
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Source: author
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Road centerlines
1. Query database to get all traces ordered by date and accuracyi. Most recent firstii. Most accurate first
Source:author
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Road centerlines
2. For each point k of each trace j ():
1. Identify nearby points All points that intersect a buffer around
Source:author
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Road centerlines
2. Points with a direction of movement different than are discarded set
3. How to analyze the set to find the road centerline?
Source:author
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Road centerlines It is assumed that it is only possible to find an
approximated solution
Road centerline = weighted combination between: Date of recording; Accuracy; Distance from a candidate solution to all points
selected ( set).
Chosen algorithm: evolutive algorithm
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Road centerlines
: candidate solution : set of selected points : influence of time (date of recording) : influence of accuracy : influence of distance
𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
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Road centerlines
, , : Multiply the value of the corresponding influence to prioritize desired characteristics
𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
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Road centerlines
Recent traces: weight closer to 1 Older traces: weight closer to 0
𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
𝐼𝑇 (𝑆𝑖)=𝑡𝑚𝑎𝑥−|𝑇 (𝑆 𝑖 ,𝑆𝑟 )|
𝑡𝑚𝑎𝑥
Influence of Time
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Road centerlines𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑
𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
𝐼𝐴 (𝑆𝑖 )=𝛼 𝐴 (𝑆 𝑖 )2+(−1−𝛼 ∙𝑎𝑚𝑎𝑥
2
𝑎𝑚𝑎𝑥) 𝐴 (𝑆𝑖 )+1
𝛼=−𝑎𝑙𝑖𝑚−𝑎𝑚𝑎𝑥 (𝑎𝑙𝑖𝑚𝑉−1)𝑎𝑚𝑎𝑥 𝑎𝑙𝑖𝑚 (𝑎𝑚𝑎𝑥−𝑎𝑙𝑖𝑚)
Influence of Accuracy
Wei
ght
Accuracy
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Road centerlines𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑
𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
𝐼𝐷 (𝐶𝑥 ,𝑆𝑖 )=𝛽𝐷 (𝐶𝑥 ,𝑆𝑖 )2+(−1− 𝛽 ∙𝑑𝑚𝑎𝑥
2
𝑑𝑚𝑎𝑥)𝐷 (𝐶𝑥 ,𝑆𝑖)+1
𝛽=−𝑑𝑙𝑖𝑚−𝑑𝑚𝑎𝑥 (𝑑𝑙𝑖𝑚𝑉−1)𝑑𝑚𝑎𝑥𝑑𝑙𝑖𝑚 (𝑑𝑚𝑎𝑥−𝑑𝑙𝑖𝑚)
Influence of Distance
Wei
ght
Distance
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Road centerlines𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑
𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
Source:author
𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷Closer to highest
concentration of points: smallest overall distance
Closer to points high better accuracy
𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (𝐶𝑥 )=∑𝑖=1
𝑛 ′ ′
𝐼𝑇 (𝑆𝑖 ) ∙𝑀𝑇 +𝐼𝐴 (𝑆𝑖 ) ∙𝑀𝐴+𝐼𝐷 (𝐶 𝑥 ,𝑆 𝑖 ) ∙𝑀𝐷
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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:
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Road centerlines Evolutive algorithm finds centerline close to Next step: repeat process for If has already been used, skip to the next point
Source:author
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Results Implemented in Python DB: PostgreSQL + PostGIS
Data collected between 27/01/2013 e 15/06/2013 4237 traces 966698 points
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Results Tests: comparison between
Proposed method’s results Satellite images
Google Earth
Executed on places with complex road structures
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Tests (1)
Roads intersect
Source: Google Earth / author
Satellite image
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Tests (1)Points collected (filtered)
Source: Google Earth / author
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Tests (1)
Way centerline
Direction of movement differentiates traces
It is possible to improve filtering...
Final result
Source: Google Earth / author
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Tests (2)
Roads with different direction of movement
Roads with same direction of movement
Satellite image
Source: Google Earth / author
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Tests (2)Points collected (filtered)
Source: Google Earth / author
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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
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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
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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
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Future work Improve collected traces’ reliability
Ex.: Kalman Filter Different update policies for each region
Downtown: more data, only accept better accuracy Rural areas: less data, accept older data
Mining more information Traffic lights Pot holes
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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”. <http://garmin.blogs.com/ my_weblog/2010/09/garmin-asus-around-the-globe.html> (accessed on Nov 22).
GFK (2013) “GfK TEMAX BRASIL T2 2013: Crescimento no mercado com forte influência de materiais de escritório e periféricos”. <http://www.gfk.com/br/news-and-events/press-room/press-releases/Paginas/TEMAX-BRASIL-T2-2013.aspx> (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 fromgeoreferenced smartphone data
XIV Brazilian Symposium on GeoInformatics (GEOINFO 2013)
George H. R. Costa, Fabiano Baldo{dcc6ghrc, baldo}@joinville.udesc.br