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

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

N C R S T Asset Management #3 Centerline Applications

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

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

N C R S T Asset Management #6 GPS for Hwy Ops Hi end Lo end

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

N C R S T Asset Management #8 Lane Discrimination Test

N C R S T Asset Management #9 Lane Discrimination Test

N C R S T Asset Management #10 Lane Discrimination Test

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

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

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

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

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

N C R S T Asset Management #16 Multispectral sensors Reflectance  Infra-red Wavebands originally optimized to sense health of Soviet wheat

N C R S T Asset Management #17 Hyperspectral sensors Reflectance … 2400 Each pixel is characterized by 200+ reflectance values

N C R S T Asset Management #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)

N C R S T Asset Management #19 ASD full range spectrometer Field Spectrometer

N C R S T Asset Management #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

N C R S T Asset Management #21 Concretes

Concrete roof Parking lot Asphalt road

N C R S T Asset Management #24 Step 1 result MESMA

N C R S T Asset Management #26 Step 2 result Q-tree

N C R S T Asset Management #27 Step 3 result Vectorize

N C R S T Asset Management #28 Step 3 result

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

N C R S T Asset Management #30 Road condition

N C R S T Asset Management #31 Field Data Records

N C R S T Asset Management #32 Surface Treatments

N C R S T Asset Management #33 Age

N C R S T Asset Management #34 Surface “Quality”

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

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