Remote Sensing for Asset Management

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 e.g., 110,000 miles of road in Iowa • Inventory of large systems costly 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 onroad 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 point Anchor section • Anchor sections – Measurement of distance between anchor points along roadway Datum Accuracy Requirements Anchor points  ± 1.0 meter Anchors sections  ± 2.1 meter Common Business Data Items • HPMS requirements • Additional Iowa DOT elements  Section Length  Number of Through Lanes  Surface/Pavement Type  Lane Width  Access Control  Median Type  Median Width  Parking  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 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) SE corner of intersecting sidewalks • 2 easily identified features selected – Could be identified in all 4 datasets – Had a distinct point to locate 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 & 2inch 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 collected at ± 10 m) (but 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 Typical Segment on Dakota (imagery and DGPS) Deviation from datum (m) 20.0 18.0 16.0 Deviation From Datum (meters) 14.0 12.0 Roadware 1 10.0 8.0 6.0 4.0 2.0 0.0 Roadware 2 Videolog • Compared centerline representation of 3 methods – Imagery – VideoLog DGPS – Roadway DGPS 0 181 362 543 724 905 1086 1267 1448 1628 1809 1990 2171 2352 2533 2714 2895 3076 3257 3438 3619 3800 3981 4162 Distance Along Segment (meters) 4343 Establishing Roadway Centerline Worst Alignment on Union (DGPS) 20.0 18.0 16.0 Deviation From Datum (meters) 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 0 74 149 223 298 372 447 521 596 670 Distance Along Segment (feet) 24-Inch 1-Meter Roadware 1 Roadware 2 Deviation from datum (m) 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?