1. Applications of Remote Sensing in Transportation

2. Use of Remote Sensing Feature Extraction –Evaluate ability to identify and measure access- related features from aerial photographs at different resolutions. –Evaluate the feasibility of deriving 3D roadway characteristics.

3. Feature Extraction (Asset management ) 6” image Signal Structure Turning Lane Characteristics

4. Data elements collected HPMS 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 Section Length Number of Through Lanes Surface/Pavement Type Lane Width Access Control Median Type Median Width Parking

5. Pilot Study Corridor Ames, Iowa

6. Observer Variation in Feature Recognition An unique feature identified by different observers

7. Linear Measurement Error: 2-Inch Dataset Linear Measurement Error: 6-Inch Dataset Linear Measurement Error: 2-Foot Dataset

8. 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

9. Pavement Grade Extraction ( Iowa 1) TIN developed from the LIDAR points. LIDAR data defining the roadway are identified by using the Ortho-photos. Maximum slope representation

10. Regression Analysis Perpendicular distance from the centerline is computed and the elevation values obtained from LIDAR are used to estimate the elevation variation on the road surface which is characterized by the cross-slope and grade. Y(Elevation) = A1*Cross-Slope + A2*Grade +Constant A1= Centerline distance, A2= Distance along the road

11. Regression Analysis Results and Future work South Bound directionNorth Bound Direction Cross Slope 1.2%-3.9% Grade 0.38%0.3% The cross-slopes derived from the LIDAR can be improved by an accurate definition of the centerline. The estimated cross-slope and grade would be compared to ground survey data to improve the estimation.

12. Thank you