inferring road maps from gps traces

James Biagioni and Jakob Eriksson

Survey and Comparative Evaluation

Inferring Road Mapsfrom GPS Traces

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Road surveys

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Rural/developing areas

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New road construction

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Road closures

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Crowd-sourced GPS data

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GPS traces

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Inferred road map

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Contributions‣ Quantitative evaluation method

‣ Survey of existing literature

‣ Comparative evaluation

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Edelkamp & Schrödl

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Drop seeds

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Adjust seeds

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Link seeds

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Cao & Krumm

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Pre-processing

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Add new edges

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Match edges

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Prune spurious edges

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Davies, Beresford & Hopper

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Create 2-D histogram

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Density estimate

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Apply threshold to image

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Extract road centerline

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Raw data

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Raw data (low-error)

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Edelkamp (low-error)

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Cao (low-error)

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Davies (low-error)

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Raw data

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Raw data (high-error)

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Edelkamp (high-error)

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Cao (high-error)

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Davies (high-error)

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Quantitative Evaluation

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Ground truth map “holes”

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Generated map “marbles”

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Marbles fall into holes

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Marbles vs. holes

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Map comparison metrics

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Overall performance

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Overall performance

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Parameter tuning

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Observation validation

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Quantitative results

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Enhanced Davies

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Performance summary

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Algorithm Full data set Low-error data set

Edelkamp 0.60 0.75

Cao 0.65 0.70

Davies 0.70 0.80

Davies Enhanced 0.75 0.85

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Summary‣ Three representative algorithms

‣ Qualitative evaluation

‣ Quantitative evaluation method

‣ Quantitative results

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Questions?Thanks!

Dataset and implementations available at bits.cs.uic.edu