1. Val Noronha University of California, Santa Barbara Centerline Extraction and Road Condition

2. N C R S T Asset Management 2001-09-23 #2 Why centerlines?  Accurate (x,y) for ITS precision applications location based services  Accurate length for compatibility with linear referencing

3. N C R S T Asset Management 2001-09-23 #3 Centerline Applications

4. N C R S T Asset Management 2001-09-23 #4 Approaches to deriving centerlines  Convert old maps  Convert new maps, integrate CAD plans  Photogrammetry  GPS

5. N C R S T Asset Management 2001-09-23 #5 Outline  Centerlines from GPS  Centerlines from hyperspectral imagery  Other uses of hyperspectral analysis: early findings on road condition

6. N C R S T Asset Management 2001-09-23 #6 GPS for Hwy Ops Hi end Lo end

7. N C R S T Asset Management 2001-09-23 #7 Low-end GPS units $250$195$150

8. N C R S T Asset Management 2001-09-23 #8 Lane Discrimination Test

9. N C R S T Asset Management 2001-09-23 #9 Lane Discrimination Test

10. N C R S T Asset Management 2001-09-23 #10 Lane Discrimination Test

11. N C R S T Asset Management 2001-09-23 #11 The one to beat … $150 at CompUSA  Convenience  Price  Can RS beat this?

12. N C R S T Asset Management 2001-09-23 #12 Remote sensing centerline strategy  Find pixels that represent road … hyperspectral library  Detect linear patterns, form centerlines  Attach legacy attributes  Compare costs and benefits

13. N C R S T Asset Management 2001-09-23 #13 3-step hyperspectral process MESMAQ-treeVectorize Additional steps: clean, revisit, conflate

14. Easy Street  New neighborhood  Little or no foliage overhang  Vehicles in garage/driveway

15. Not so easy  Repairs and surface coats  Paint stripes  Shadows  Parked vehicles  Foliage overhangs

16. N C R S T Asset Management 2001-09-23 #16 Multispectral sensors Reflectance 400700  20 5040 Infra-red Wavebands originally optimized to sense health of Soviet wheat

17. N C R S T Asset Management 2001-09-23 #17 Hyperspectral sensors Reflectance 400700 203050 … 2400 Each pixel is characterized by 200+ reflectance values 203020305020302030502030205030

18. N C R S T Asset Management 2001-09-23 #18 Hyperspectral road identification  Materials have unique hyperspectral signatures, based on chemistry, texture, etc  What are the principal materials found in roads … what are their signatures?  Study them at close range in the field (handheld spectrometer)  Then see if you can detect the signatures from imagery (4m airborne AVIRIS by JPL)

19. N C R S T Asset Management 2001-09-23 #19 ASD full range spectrometer Field Spectrometer

20. N C R S T Asset Management 2001-09-23 #20  499 roof  179 road  66 sidewalk  56 parking lot  40 road paint  37 vegetation Field Spectra Collected  47 non-photosynthetic vegetation (bark, dead wood)  27 tennis court  88 bare soil and beach  50 miscellaneous other urban spectra

21. N C R S T Asset Management 2001-09-23 #21 Concretes

23. Concrete roof Parking lot Asphalt road

24. N C R S T Asset Management 2001-09-23 #24 Step 1 result MESMA

26. N C R S T Asset Management 2001-09-23 #26 Step 2 result Q-tree

27. N C R S T Asset Management 2001-09-23 #27 Step 3 result Vectorize

28. N C R S T Asset Management 2001-09-23 #28 Step 3 result

29. N C R S T Asset Management 2001-09-23 #29 Where it Fits in the Big Picture Global scale — Logistics Local scale, esp urban — Asset mgmt

30. N C R S T Asset Management 2001-09-23 #30 Road condition

31. N C R S T Asset Management 2001-09-23 #31 Field Data Records

32. N C R S T Asset Management 2001-09-23 #32 Surface Treatments

33. N C R S T Asset Management 2001-09-23 #33 Age

34. N C R S T Asset Management 2001-09-23 #34 Surface “Quality”

35. N C R S T Asset Management 2001-09-23 #35 In conclusion …  RS for centerlines a fully automated solution is not yet here potential for the future  RS for road condition much promise

36. 1 www.ncgia.ucsb.edu/ncrst