Remote Sensing for Asset Management

Shauna Hallmark Kamesh Mantravadi

David VenezianoReginald Souleyrette

September 23, 2001Madison, WI

The Problem/Opportunity

• DOT use of spatial data– Planning– Infrastructure Management– Traffic engineering– Safety, many others

• Inventory of large systems costly– e.g., 110,000 miles of road in Iowa

The Problem/Opportunity

• Current Inventory Collection Methods– Labor intensive– Time consuming– Disruptive– Dangerous

Data Collection Methodologies

• Manual (advantages/disadvantages)• low cost• visual inspection of road• accurate distance measurement• workers may be located on-road• difficult to collect spatial (x,y)

• Video-log/photolog vans (advantages/disadvantages)• rapid data collection• digital storage • difficult to collect spatial

(x,y)

Data Collection Methodologies

• GPS (advantages/disadvantages)

• highly accurate (x,y,z)• can record elevation• time consuming if high

accuracy is required• workers may be located

on-road

Data Collection Methodologies

• Remote sensing (advantages/disadvantages)

• Data collectors not located on-site• Initially costly but multiple uses• Can go back to the images

Research Objective

• Can remote sensing be used to collect infrastructure inventory elements?

• What accuracy is possible/necessary?

Remote Sensing• "the science of deriving information

about an object from measurements made at a distance from the object without making actual contact” Campbell, J. Introduction to Remote Sensing, Second Edition.

• Applications in many fields such as forestry, Oceanography, Transportation

Remote Sensing• 3 types

1) space based or satellite• Images acquired from space

2) airplane based or aerial• Images acquired form aerial platforms

like high, low altitude airplanes and balloons. (USGS)

3) in-situ or video/magnetic

Research Approach• Identify common inventory features • Identify existing data collection methods• Use aerial photos to extract inventory

features • Performance measures• Define resolution requirements• Recommendations

Application

• Use of Remote sensing to collect features for the Iowa DOT’s Linear Referencing System (LRS)

• Datum– Anchor points– Anchor sections

• Business data– Inventory features

Datum

• Anchor points– Physical entity– (X,Y) – Intersection of 2

roadways– Intersection of RR and

roadway– Edge of median– Bridges

• Anchor sections– Measurement of

distance between anchor points along roadway

Anchor point

Anchor section

Datum Accuracy Requirements

Anchor points ± 1.0 meter

Anchors sections ± 2.1 meter

Common Business Data Items

Shoulder Type Shoulder Width

Right and Left Number of Right/Left

Turn Lanes Number of Signalized

Intersections Number of Stop

controlled Intersections Number of Other

Intersections

• HPMS requirements• Additional Iowa DOT

elements Section Length Number of Through

Lanes Surface/Pavement

Type Lane Width Access Control Median Type Median Width Parking

Imagery Datasets

• 2-inch dataset - Georeferenced• 6-inch dataset - Orthorectified• 2-foot dataset – Orthorectified• 1-meter dataset – Orthorectified –

simulated 1-m Ikonos Satellite Imagery

* not collected concurrently

Performance Measures

• Establishing geographic location of anchor points and business data– Positional accuracy – Variation between operators for locating

elements (Operator Variability)– Ability to recognize features in imagery

(Feature Recognition)

• Calculation of anchor section lengths• Establishing roadway centerline

Positional Accuracy

• Root Mean Square (RMS)

• Imagery position vs. position w/ GPS (centimeter horizontal accuracy)

• 2 easily identified features selected– Could be identified in

all 4 datasets– Had a distinct point to

locate

SE corner of intersecting sidewalks

SE corner of drainage structure

Positional Accuracy

• 2-inch, 6-inch, 24-inch met accuracy requirements of Iowa DOT LRS for anchor points

• Even for 1-meter RMS < 2 meters

• 95% of points were located within < 3.5 meters for all datasets --- sufficient accuracy for most asset management applications

Operator Variability

• For manual location of features

• How much of spatial error can be attributed to differences in how data collectors locate objects

Variation among observers in spatially

locating a point

Operator Variability• 7 operators located 8

sets of features– Traffic signal posts – Drainage structures– Pedestrian crossings– Center of intersections– Center of driveways– RR crossings– Bridges– Medians

Edge of drainage structure as located by 7 operators

• Specific instructions for locating (i.e. SE corner of bridge)

• Compared variability among observers

Operator Variability (results)

• Only 3 features could be identified consistently in all 4 datasets– Driveways --- RR Crossings– Center of intersections

• 5 other features identified in 6-inch & 2-inch datasets

Operator Variability (results)

• Certain features, such as railroad crossings, could be located with less variation than features such as driveway centers (less distinct)

• mean variability < 0.5 meters– Drainage structures, driveways, traffic signal posts, pedestrian

crossings (2 and 6-inch tested only)

• mean variability >= 0.5 m & < 1.0 m– Medians (2 & 6-inch tested only, RR crossings)

• mean variability >= 1.0 m– Intersections, bridges

• Significant variability in features used as anchor points

• Variability ~ allowed error (1.0 meter)

Feature Identification• Points can be located within

allowance for anchor points (± 1.0 m) for all but 1-meter

• Even 1-meter rms < 2.0 meters, sufficient for most asset-related applications

• But can features be consistently recognized

IP (%) = (Fa/Fg) * 100

• % of features recognized in imagery compared to ground count

Extraction of features from 6-inch image

Feature Identification

Feature Identification

• Of 21 features– 2-inch: 100% identified consistently – 6-inch: > 80% identified consistently

• Signs, median type, stopbars, utility poles

– 24-inch: < 50% consistently identified• 6 features not identified at all

– 1-meter: < 25% consistently identified• 8 features not identified at all

Calculation of anchor section lengths

• Linear measure along roadway centerline between anchor points

• Iowa DOT LRS requires ± 2.1 m

• Established centerline and measured for 7 test anchor section test segments

• Compared against DMI

values from Iowa DOT LRS Pilot Study• Also collected distance using Roadware DMI

van (but collected at ± 10 m)

Anchor Section Results

• None of the methods met ± 2.1 m RMS required for anchor section distances

**** Iowa DOT study found 6-inch met accuracy requirement ***

• All imagery: RMS < 8 meters

• All imagery: mean < 2 m

Establishing Roadway Centerline

• Compared centerline representation of 3 methods– Imagery– VideoLog DGPS– Roadway DGPS

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0

181

362

543

724

905

1086

1267

1448

1628

1809

1990

2171

2352

2533

2714

2895

3076

3257

3438

3619

3800

3981

4162

4343

Distance Along Segment (meters)

Dev

iati

on

Fro

m D

atu

m (

met

ers)

Roadware 1

Roadware 2

Videolog

Typical Segment on Dakota (imagery and DGPS)

Deviation from datum (m)

Establishing Roadway Centerline

Worst Alignment on Union (DGPS)

Deviation from datum (m)

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0 74 149 223 298 372 447 521 596 670

Distance Along Segment (feet)

Dev

iati

on

Fro

m D

atu

m (

met

ers)

24-Inch

1-Meter

Roadware 1

Roadware 2

DGPS Traces from Iowa DOT LRS Pilot Study

Nevada, IA

Conclusions

• Most significant issue with imagery– At lower resolutions, difficult to identify features

• Spatial accuracy for all imagery datasets comparable

• Limiting factor is ability to consistently identify features

• Minimum of 6-inch required for identification of features

• 1-meter or 24-inch:– for measurement of centerline– Identification of large features

Questions?