Lecture25 LiDAR

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!



CE 2010


Today’s Agenda

Test 2

%inal &ro'ect "elivera#les

"esign (or)sho*+emote Sensing ,**lications in Civil

Engineering

Li",+



CE 2010


Test #2

,verage• -./

Stanar"eviation• 10.1



CE 2010


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



CE 2010


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.



CE 2010


Remote Sensing &'ategories(

&assive• ,erial &hotogra*hy78magery

• 9ultis*ectral 8magery

,ctive• +aar

• Li",+



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



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



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



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





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





: 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



i i ihi



: .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 model of rep "ridge dec%surface

P i i iA hi






: /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



: Calculating volumeof spalldev

algorithm': A"le to

calculatevolume for

di$cult toreach tall'locations

15




P i itiA hi t



/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



PrioritiesAchie ements



/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



17

PrioritiesAchievements



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






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






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






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


+maging ,P1 5 Synthetic Aperture

1adar SA1'





+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






@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






+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'



()ample of 45D roadway data created with

+nSA1




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



Achievements DSS



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-


DSS3ield DemoAssessment



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=




LiDAR Data Ac*isition




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()




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




Learning 'atalytics




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




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




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




S"ading by -levation




"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




&/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.




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




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




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=




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




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=



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=

Date post:	04-Mar-2016
Category:	Documents
Upload:	adamnaidorf
View:	233 times
Download:	0 times

Lecture25 LiDAR

Documents