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Sensing Highway Surface Conditions with High-Resolution Satellite Imagery

Sensing Highway Surface Conditions with High-Resolution Satellite Imagery. Ashwin Yerasi, University of Colorado William Emery, University of Colorado.

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Sensing Highway Surface Conditions with High-Resolution Satellite Imagery

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  1. Sensing Highway Surface Conditions with High-Resolution Satellite Imagery Ashwin Yerasi, University of Colorado William Emery, University of Colorado DISCLAIMER: The views, opinions, findings and conclusions reflected in this presentation are the responsibility of the authors only and do not represent the official policy or position of the USDOT/RITA, or any State or other entity.

  2. Motivation • In situ surveillance of highway surfaces • Slow and tedious • Analysis done by eye • Limited coverage • Remote sensing of highway surfaces • Comparatively quick and effortless • Analysis done by machine • Full area coverage • Latter technique can be used as a precursor to the former or as a compliment

  3. In Situ Data • Provided by the Colorado Department of Transportation • Collected by Pathway Services Inc. • Road parameters of interest • Roughness (IRI) • Rutting • Cracking (fatigue, etc.) • Remaining service life • >10 years: Good • 5-10 years: Fair • <5 years: Poor >10 years: Good 5-10 years: Fair <5 years: Poor Pathway Services Inc. Surveillance Van

  4. Remotely Sensed Data • Provided by DigitalGlobe • Collected by WorldView-2 spacecraft • Panchromatic images • 450-800 nm • Spatial resolution 46-52 cm • 11-bit digital numbers WorldView-2

  5. Digital Number 21B 115A 24A

  6. Digital Number

  7. Occurrence-Based Texture Filtering Filtered Image Original Image

  8. Data Range Example of a homogeneous surface typical of good pavement Homogeneous Window Filtered Pixel Example of a homogeneous surface typical of poor pavement Heterogeneous Window Filtered Pixel

  9. Data Range 21B 115A 24A

  10. Data Range

  11. Mean Example of a homogeneous surface typical of good pavement Homogeneous Window Filtered Pixel Example of a homogeneous surface typical of poor pavement Heterogeneous Window Filtered Pixel

  12. Mean 21B 115A 24A

  13. Mean

  14. Variance Example of a homogeneous surface typical of good pavement Homogeneous Window Filtered Pixel Example of a homogeneous surface typical of poor pavement Heterogeneous Window Filtered Pixel

  15. Variance 21B 115A 24A

  16. Variance

  17. Entropy Example of a homogeneous surface typical of good pavement Homogeneous Window Filtered Pixel Example of a homogeneous surface typical of poor pavement Heterogeneous Window Filtered Pixel

  18. Entropy 21B 115A 24A

  19. Entropy

  20. Conclusions • Highway pavement becomes lighter in panchromatic grayscale shade as it degrades • Digital number increases • Mean increases • Highway pavement becomes less uniform as it degrades • Data range increases • Variance increases • Entropy increases • These changes are detectable through satellite remote sensing techniques and can likely be used to classify road surface conditions such as good, fair, poor and to justify repaving needs

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