Upload
adamnaidorf
View
242
Download
0
Embed Size (px)
DESCRIPTION
This lecture is from CE 2300, an introduction to Civil Engineering course taught at UVA. This specific lecture was taught by professor Dr. Brian Smith in 2015.
Citation preview
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!
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 2/43
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",+
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 3/43
CE 2010
Civil Engineering Techniques
Test #2
,verage• -./
Stanar"eviation• 10.1
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 4/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 5/43
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.
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 6/43
CE 2010
Civil Engineering Techniques
Remote Sensing &'ategories(
&assive• ,erial &hotogra*hy78magery
• 9ultis*ectral 8magery
,ctive• +aar
• Li",+
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 7/43
Project Concept
7
DSS: algorithms & interfaceDecision Support System
Concept)ln( siii
L L X −=
( ) ( ) r d er R
er k s
r r k jkR j
′′≈ ′⋅−
∫ ˆ2
2
4, ρ
π
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 8/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 9/439
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 10/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 11/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 12/4312
: 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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 13/43
: .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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 14/4314
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 15/43
: 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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 16/43
/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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 17/43
/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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 18/4318
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 19/4319
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 20/4320
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 21/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 22/43
+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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 23/43
@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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 24/43
+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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 25/4325
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$ciency calculationsvia satellite imagery
C!rrentl": #e will "eassessing this technologyas part of the 9elddemonstrations 6 ensurecareful use of funds if
purchasing commercialsatellite imagery
PrioritiesCommercial Sensor (valuation3easi"ility2-a" Studies
AchievementsProject ProgressChallenges
Achievements DSS
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 26/43
Decision Support System +ntegration
26
Promising Technologies
3-D Optics including Photogrammetry → dec% and girder surface challengesincluding some glo"al metrics
Thermal Infrared → dec% and girder surface challenges including somesu"surface issuesDigital Image Correlation → glo"al metrics
Radar including SAR InSAR → dec% and girder su"surface challenges includingsome glo"al metricsStreet-!ie" Style Photography → dec% and girder surface challenges includingsurface roughness metricSatellite Imagery → dec% and girder surface challenges including some glo"almetrics
*istoricalBridge$
Specic'nformation
BridgeStandards and
Re+!irements
'ntegratedBridge
ssessment
DecisionS!pportS"stemData analysis
+ntegration Algorithms
BR'D,-S',.TR-
AchievementsProject ProgressChallenges
DSS3ield DemoAssessment
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 27/43
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=
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 28/43
CE 2010Civil Engineering Techniques
LiDAR Data Ac*isition
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 29/43
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()
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 30/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 31/43
CE 2010Civil Engineering Techniques
Learning 'atalytics
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 32/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 33/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 34/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 35/43
CE 2010Civil Engineering Techniques
S"ading by -levation
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 36/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 37/43
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.
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 38/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 39/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 40/43
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=
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 41/43
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
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 42/43
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=
7/21/2019 Lecture25 LiDAR
http://slidepdf.com/reader/full/lecture25-lidar 43/43
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=