Remote Sensing for Asset Management Shauna Hallmark Kamesh Mantravadi David Veneziano Reginald...

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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

• GPS (advantages/disadvantages)

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

accuracy is required• workers may be located

on-road

• 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

• 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

• 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

Distance Along Segment (meters)

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 74 149 223 298 372 447 521 596 670

Distance Along Segment (feet)

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?

Remote Sensing for Asset Management Shauna Hallmark Kamesh Mantravadi David Veneziano Reginald...

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