Remote Sensing for Asset Management Shauna Hallmark Kamesh Mantravadi David Veneziano Reginald Souleyrette September 23, 2001 Madison, 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

The Problem/Opportunity Collect transportation inventories through remote sensing Improve existing procedures Exploit new technologies Extract data which was previously difficult and costly to obtain

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 (%) = (F a /F g ) * 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

Extraction Procedures Turning Lane Characteristics 6” image

Extraction Procedures Signal Structure 6” image

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 Typical Segment on Dakota (imagery and DGPS) Deviation from datum (m)

Establishing Roadway Centerline Worst Alignment on Union (DGPS) Deviation from datum (m)

DGPS Traces from Iowa DOT LRS Pilot Study Nevada, IA

Problems/Difficulties Different data sources –Taken on different days –Saved in different formats (.tif,.sid) –All sets are panchromatic, no color Potential photo errors –Atmospheric distortions –Camera displacements at time of exposure

Problems/Difficulties Vegetation can block the view of features Impossible to begin and end measurements on images at the same points as were used in the field Pavement markings heavily relied upon for length and width measurements, but these are not repainted in the exact location

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?