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Ortho & LiDAR

Ortho & LiDAR. Accuracy Assessment. Mike Tully President & CEO, Aerial Services, Inc. Cedar Falls, Iowa. FAIL. George Washington Bridge, NY. FAIL. FAIL. FAIL. Now What? . Take Away: Why you should care about positional accuracy !. “Intended Use”. What do you mean by

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Ortho & LiDAR

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  1. Ortho & LiDAR Accuracy Assessment Mike Tully President & CEO, Aerial Services, Inc. Cedar Falls, Iowa

  2. FAIL

  3. George Washington Bridge, NY FAIL

  4. FAIL

  5. FAIL

  6. Now What?

  7. Take Away: Why you should care about positional accuracy!

  8. “Intended Use”

  9. What do you mean by “accuracy”? Accuracy Consistency Others Precision Completeness Currency

  10. Accuracy Defined Simple & Sweet

  11. How Tall Are You? “Measurement Error is InEscapable

  12. Measuring Error Surveyed XYZ ∆R ∆Y Image XYZ ∆X

  13. Quantifying Error

  14. Probability & Confidence ±2 SD 95% ±1 SD 68% Mean Below Mean Above Mean

  15. NMAS 2’ Contours to NSSDA Standards Mean NMAS 2’ Contours requires ½ Contour Interval = 1.0’ Accuracy. NSSDA with 95% Confidence requires 1 RMSE of 0.61’ 2 SD Frequency 95% Confidence Observed Error in Z Actual Data

  16. Non-”Normal” Skewed “Peakedness” “Normal”

  17. Accuracy Standards

  18. Accuracy Standards

  19. Changing Technology … Evolving Standards Orthos (GSD) Lidar (points/sq m)

  20. Next-Gen Standards ASPRS [Draft] Accuracy Standards for Digital Geospatial Data, 2012 Includes a new high accuracy level “X” Class IIA = USGS minimum vertical accuracy (unclassified lidar)

  21. Assessing Accuracy Ortho & Lidar

  22. Advanced Visual Metrics Visual inspection of hydro-flattening seems to be predominant quality metric. There are BETTER ways!

  23. Error Reporting: Lidar & Orthos SIS Topo Analyst™ Co-Registered LiDAR and Ortho accuracies are reported.

  24. LiDAR Accuracy Metrics COVERAGE OVERLAPS

  25. LiDAR Accuracy Metrics Point Density Plots Water Voids / Holes

  26. LiDAR Accuracy Metrics Relative Accuracy (RMSD) Z-diff Rasters

  27. LiDAR Accuracy Control Point Errors Metadata Errors Classification Errors Error Distribution Rigorous Statistics Automated QA / Verification

  28. How do you know it’s good enough?

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