7/21/2019 Lecture25 LiDAR http://slidepdf.com/reader/full/lecture25-lidar 1/43 CE 2010 Civil Engineering Techniques Brian L. Smith University of Virginia LiDAR + Remote Sensing Applications Lecture 2! "ecem#er 1$ 201!
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CE 2010
Civil Engineering Techniques
Brian L. Smith
University of Virginia
LiDAR + Remote Sensing
Applications
Lecture 2!
"ecem#er 1$ 201!
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CE 2010
Civil Engineering Techniques
Today’s Agenda
Test 2
%inal &ro'ect "elivera#les
"esign (or)sho*+emote Sensing ,**lications in Civil
Engineering
Li",+
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CE 2010
Civil Engineering Techniques
Test #2
,verage• -./
Stanar"eviation• 10.1
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CE 2010
Civil Engineering Techniques
Final Project Deliverables
g file
*f file
(or "ocument ouTu#e Vieo
Deliver all via collab by 12:30pmTuesday, December 8 • This is a hard deadline.
•
Late submissions are NT accepted
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CE 2010
Civil Engineering Techniques
Design or!s"op on T"rsday$
December %
Sen .*f3s of your raft *lan sheet to"avi +echt #y /400 a.m. Thursay
morning Volunteers ill 5*resent6 their *lans an
grou* ill critique
"avi ill also #e availa#le forassistance as time *ermits.
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CE 2010
Civil Engineering Techniques
Remote Sensing &'ategories(
&assive• ,erial &hotogra*hy78magery
• 9ultis*ectral 8magery
,ctive• +aar
• Li",+
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Project Concept
7
DSS: algorithms & interfaceDecision Support System
Concept)ln( siii
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er k s
r r k jkR j
′′≈ ′⋅−
∫ ˆ2
2
4, ρ
π
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Overview
: Achievements
: Project
progress
: Challenges: Concerns
Remote Sensing for Bridges ; Monitor and assess condition enhance inspection
; !se commercially availa"le technologies
; At a distance
; #ithout stopping tra$c or closing lanes
8
Overview AchievementsProject ProgressChallengesConcerns
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Location “Top 10” Priorities/Challenges
Dec% Surface Map crac%ing Scaling Spalling Delaminationsthrough surface crac%s' ()pansion *oint ()ternal+ssues
Dec% Su"surface Scaling Spalling Delaminations ()pansion *oint+nternal +ssues Corrosion Chloride +ngress
,irder Surface Structural Steel and Structural Concrete Crac%ingPaint Condition Steel or Concrete Section -oss
,irder Su"surface Structural Concrete Crac%ing Concrete Section -ossChloride +ngress Prestress Strand .rea%age
,lo"al Metric .ridge -ength Settlement /ransverse Movement0i"ration Surface 1oughness
/op Priorities 2 Challenges
AchievementsProject ProgressChallenges
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
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Commercial Sensor (valuation 1eport: Promising/echnologies
10
45D Optics including Photogrammetry
/hermal +nfrared
Digital +mage Correlation
1adar including SA1 and +nSA1
Street5view Style Photography
Satellite +magery and Aerial Photography
-iDA1
3ield +nspection of .ridges 6 shadowed "ridgeinspectors for various "ridge types to "etterunderstand how these technologies can "epractically implemented for enhancing inspections
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
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45D OpticsDenition: Any digital
photography in theoptical thermal infraredand near infrared parts of
the spectrum collectedfrom an aerial satellite orother platform
Proposed pplication:Mapping "ridge features74D models7 characteri8ingdec% surface spallingcrac%s'
11
C!rrentl": using DS-1cameras
Stereo overlapping of photosand 45D modeling software
creates a point cloud
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
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: Preliminary wor% 5 whatcan "e measured in the9eld
: System is "eing designed
with low5cost componentsDigital S-1s commercialclose5rangephotogrammetry software'5 -ow cost alternative for 45
D data alt -iDA1'
: ;ow to "est transfer thisinformation to the "ridgeinspector 6 visuali8ing
results
Spalls located under the"ridge dec%
Models generated from the in9eld photoswith te)tured model on the left and shadedmodel output from PhotoScan on the right
45D Optics 6 3ield /esting
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
i i ihi
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: .ridge dec%representative surface 6method of visuali8ingsurface roughness
algorithm': !se 45D surface to
create automatedanalysis of surfacecondition 2 roughness
: Also calculating volumeof spall dev algorithm'
: +ntegrate results intoDSS
13
45D Optics 6 3ield /esting
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
45D model of rep "ridge dec%surface
P i i iA hi
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45D Optics 6 3ield /esting
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
: /urning 45D surfaceinformation intouseful informationon overall dec%
condition: ()ample 6
imagery D(Msurface deviationfrom a <at planedeviation2roughness "y analysis regionchangea"le'
P i itiA hi t
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: Calculating volumeof spalldev
algorithm': A"le to
calculatevolume for
di$cult toreach tall'locations
15
45D Optics 6 3ield /esting
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
P i itiA hi t
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/hermal +1Denition:
Measuring the radianttemperature of the concretedec% "y thermal infraredcamera anomalies interruptthe heat transfer through theconcrete'
Surface delaminations will "eappeared as hot spots on thethermal +1 image
Progress#
-a"oratory demonstrations toinvestigate surface and
su"surface defects
Proposed pplication:
-ocating delaminations and othersu"surface defects
16
=>:>?
==:@?
Time$dependent
e%ects
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchie ements
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/hermal +1 6 +nitial /esting
Specimen &ithsim!lateddefects
Thermal 'RLa(orator"Set!p
: Cold sla"s were "rought in the la" which hassigni9cantly higher temperature than outside andthermal +1 images were ta%en inside the la" which hadalmost steady environmental condition
Thermal 'R'mage
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
17
PrioritiesAchievements
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Digital +mage Correlation D+C'
Specimen
D+CCamera
-OAD
MA/-A. SoftwareAnalysis
+mage = +mage @+mages Captured from
Camera
-ayout of D+C Process
Denition: techniueconsisting of correlatingpi)els on optical images todetermine variations
Proposed pplication:,lo"al responsemovement settlement
vi"ration'7 4D models7
C!rrentl": using S-1cameras on specimens andprocess images incomputer software
algorithms such as MA/-A.
Measured System1esponse
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchievements
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Digital +mage Correlation D+C' 6 +nitial
/esting (nhanced
/rac%ingPattern
Post5Processed1esponse
: !sed for measuringdisplacements on a steel"eam with 9ducial mar%s
pattern': +mages from Digital S-1camera are processedthrough MA/-A.
: 3rom translation of 9ducialmar%s the "eamde<ection is measured: Potential measurement of
"eam vi"rations dynamic
measurement':
-oaded Steel
.eam
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchievements
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Digital +mage Correlation D+C' 6 +nitial
/esting
S-1 Camera
for capturingimages
Conventionalmeasurement
data collection
: Completed compression test on "ridge pylonsamples
: !sing surface roughness mar%s on consecutive
digital images of the samples to detectde<ection
: Collected conventional measurements ofstrains and deformations on samples
: Strain ,ages -oading 3rame
.ridge PylonCompression -oading
/est Setup
Pylon Surface+mage Close5!p
/ested PylonSample
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchievements
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Denition: SyntheticAperture 1adar SA1':Coherently process 13"ac%scatteringmeasurements from a
moving radar to produce a@5D or 45D' spatial imageof scene re<ectivity -owfreuency radar is used topenetrate surfaces
Su"surface re<ectionscorrespond to layer and2ordefects
Proposed pplication:
Mapping "ridgesurface2su"5surface @=
C!rrentl": using wide"andlow freuencycommerically5availa"leradar to investigatedetecta"ility of su"surface
structure and defects
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
+maging ,P1 5 Synthetic Aperture
1adar SA1'
AchievementsProject ProgressChallenges
PrioritiesAchievements
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+maging ,P1 6 /hus 3ar
@@
: Performed controlledla"oratory e)periments toassess and characteri8edetecta"ility of defects as afunction of radar parameters
: +ndenti9ed data processingtechniues such as coherentsu"traction to enhance theo"serva"ility of su"surfacefeatures and defects
: .egan planning of e)perimentsto demonstrate conceptsidenti9ed in la" on 9eld datacollections of "ridges
@ Pavers with = mm ,ap 6 .ac%ground Su"tract
@ Pavers with = mm ,ap 6 Change Detection
,ap is emphasi8ed
,apPaver = Paver @
Paversside view'
.loc% supportwall
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchievements
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@4
: Perform controlled 9eld
e)periments: Detect defects in interior
of "o) "eams: Buantify utility for
assessing su"surface spall: Develop algorithms to
enhance the detecta"ilityand characteri8ation of dec%defects in radar imagery in
conte)t of DSS: Provide output in DSS
readily usea"le "y "ridgeinspector
: ,eneral enough to wor%
with a variety ofcommercial radar sensors
+maging ,P1 6 e)t Steps
Pa)ers
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
PrioritiesAchievements
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+nterferometric SA1 +nSA1'Denition: +nSA1 e)ploits phase
dierences "etween @ or moreSA1 images to estimate heightof features Comparison of+nSA1 data from two time
periods can detect changes ingeometry and2or position
Proposed pplication: .ridge
dynamics vi"ration andstrain7 "ridge stiness7elevation surfaces D(M'7(ridge settlement and2orglo"al changes in position
@E
C!rrentl": +denti9ed algorithmsin literature for changedetection processing of +nSA1data ie PS+nSA1techniues (valuatingapplica"ility to "ridgesensing applicationSelecting (ridge to assessif settlement can "emeasured using imageryseparated in time eg did a
"ridge settle "etween @>>Fvs @>==G'
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
()ample of 45D roadway data created with
+nSA1
PrioritiesAchievements
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Satellite +magery and Aerial
PhotographyDenition: Any satellite imageryand aerial photography in thevisi"le and infrared ranges withsu$cient resolution that can"e used to remotely assess
dec% surface conditions
Proposed pplication: !sehigh5resolution imagery tocalculate indices of dec%
surface condition espcrac%ing and spalling #e will"uild from /A1!/ Study inde)of road su$ciency calculationsvia satellite imagery
C!rrentl": #e will "eassessing this technologyas part of the 9elddemonstrations 6 ensurecareful use of funds if
purchasing commercialsatellite imagery
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
Achievements DSS
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Decision Support System +ntegration
26
Promising Technologies
3-D Optics including Photogrammetry → dec% and girder surface challengesincluding some glo"al metrics
Thermal Infrared → dec% and girder surface challenges including somesu"surface issuesDigital Image Correlation → glo"al metrics
Radar including SAR InSAR → dec% and girder su"surface challenges includingsome glo"al metricsStreet-!ie" Style Photography → dec% and girder surface challenges includingsurface roughness metricSatellite Imagery → dec% and girder surface challenges including some glo"almetrics
*istoricalBridge$
Specic'nformation
BridgeStandards and
Re+!irements
'ntegratedBridge
ssessment
DecisionS!pportS"stemData analysis
+ntegration Algorithms
BR'D,-S',.TR-
AchievementsProject ProgressChallenges
DSS3ield DemoAssessment
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CE 2010Civil Engineering Techniques
"at is LiDAR)
Li",+ <Laser 8maging etection an ranging=is the technology of using *ulses of laser
<light= stri)ing the surfaces of the earth anmeasuring the time of *ulse return.
Li",+ acquisition system inclues4•
Li",+ sensor • "igital camera
• >&S
• 89U <8nertial 9easurement Unit=
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CE 2010Civil Engineering Techniques
LiDAR Data Ac*isition
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CE 2010Civil Engineering Techniques
LiDAR Point Data Format
?
@
8ntensity <0 ; 2!!=
+eturn Aum#er
Aum#er of +eturns <given *ulse= Scan "irection %lag
Ege of %light Line
<1.1= Classification
Scan ,ngle +an) </0 to /0= ; Left sie
<1.1= User "ata
<1.1= &oint Source 8"
>&S Time4 ou#le floating *oint time tag value <time of acquisition=! "ource: L#" "peci$ication, %ersion 1.1 &'''.las$ormat.or()
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CE 2010Civil Engineering Techniques
LiDAR Sensor 'apabilities
Leica >eosystems ,LS!088 9aDimum &ulse +ate4 1!0 )F
Sath (ith4 -! egrees$ full angle
G*erating ,ltitue4 200 ; H000m ,>L
+eturns4 I <first$ secon$ thir$ last=
8ntensities4 <first$ secon$ thir=
Vertical ,ccuracy4 J1! cm
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CE 2010Civil Engineering Techniques
Learning 'atalytics
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CE 2010Civil Engineering Techniques
LiDAR Derived Prodcts
"igital surface moel <"S9=• *levation model includin( ve(etation, buildin(s and ob+ects
"igital terrain moel <"T9=• *levation model 'ithout buildin(s and ve(etation
"igital elevation moel <"E9=
Triangulate 8rregular Aetor) <T8A= Contour lines illshaes Volume calculations "ata classes <*ostfiltering= Crosssection information Brea)lines
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CE 2010Civil Engineering Techniques
LiDAR Applications
%loo*lain management Aatural resource management <eD. forestry$ soils$ etc.= yrological moeling " visualiFation <security= Site selection analysis &i*eline corrior ma**ing Utility transmission line rating analysis$ vegetation
control Coastal an shoreline ma**ing
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CE 2010Civil Engineering Techniques
Advantages o, LiDAR Tec"nology
&rovies a highly accurate means ofelevation moel collection for 13 or 23 contours
,cquisition can ta)e *lace ay or nightK
shaos that are *ro#lematic in mountainousareas are not an issue ith Li",+ Unli)e *hotogra*hy$ acquisition can ta)e
*lace #elo clou coverK clou shaos no
issue Very cost effective for larger *ro'ects "oes not *rovie #rea) lines$ nor is it imagery
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CE 2010Civil Engineering Techniques
S"ading by -levation
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CE 2010Civil Engineering Techniques
"y is T"is Tec"nology -.citing)
Conventional Surveying4 1!.! years
&hotogrammetry4 1.! years
Liar4 H.- secons 1!0 )F
Time to Collect 1 Million Points
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CE 2010Civil Engineering Techniques
&/are -art"( 0odel
+emem#er ; the laser *ulses 5#ounce6off of hatever they hit.
Usually oul li)e 5#are earth6 "E9 ;the elevation of the groun ; not #uilt ornatural features a#ove the groun
Challenge ; eDtracting #are earthmoel from 5ra6 Li",+ ata.
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CE 2010Civil Engineering Techniques
Discssion 1 3ndstry
9any are convince that the 5future6 ofsurveying is in *oint clous <terrestrialor aerial=
Challenge no focuses to eDtracting theinformation from the *oint clous
Some com*anies no incor*oratehy*ers*ectral imagery hen collectingLi",+• (hy is this im*ortantM
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CE 2010Civil Engineering Techniques
4eoSpatial Soltions 5 Service 6,,erings
8magery ,cquisition• ,erial *hotogra*hy
• Satellite imagery <"igital>lo#e$ >eoEye=
• "igital imagery
Surveying• >roun control survey <geoetic netor)=
• Utility location survey• To*ogra*hic survey
• "rainage survey
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CE 2010Civil Engineering Techniques
4eoSpatial Soltions 5 Service 6,,erings
>8S ,**lication "evelo*ment• ES+8 ,rc>8S custom evelo*ment <C,TS trac)ing=
• ES+8 ,rc89S 8nternet 9a**ing
• >8S ata hosting
Li",+• Li",+ ata acquisition
• 9,+SN softare
• "igital ,ir#orne Camera System <",CSO=
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CE 2010Civil Engineering Techniques
Project 'ase Stdies
City of San "iego <C,= P-!)• -0 square milesQ 16R 1003 *ro'ect ma* scale
• 6 color igital ortho*hotogra*hy
• Color true ortho*hotogra*hy for CB" <2.2 mi2=
• "igital terrain moel <"T9=
• Li",+ ata acquisition
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CE 2010Civil Engineering Techniques
Project 'ase Stdies
Sacramento ,rea Council of >overnments<S,CG>= PI1)• 1000 square miles covering three counties
• H6 color igital ortho*hotogra*hy
• J 23 horiFontal accuracy requirement
• 23 contours
• Li",+ ata acquisition• "igital terrain moel <"T9=
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Project 'ase Stdies
200H Torino (inter Glym*ic >ames P1.m• Li",+ ata filtering <1-! classes=
• "igital terrain moel <"T9=
• " sha*efiles <#uiling foot*rints$ verticalo#structions=
• elico*ter laning Fone site selection <security=