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

2

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 algorithm

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Source: author

7

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 centerlines

𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (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)𝑎𝑚𝑎𝑥𝑎𝑙𝑖𝑚 (𝑎𝑚𝑎𝑥−𝑎𝑙𝑖𝑚)

Influence of Accuracy

Wei

ght

Accuracy

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Road centerlines

𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (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 centerlines

𝐹𝐼𝑇𝑁𝐸𝑆𝑆 (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

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:

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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