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Accuracy and Maps

Accuracy and Maps. Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science. Horizontal Accuracy.

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Accuracy and Maps

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  1. Accuracy and Maps Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science

  2. Horizontal Accuracy • As indicated in the National Standard for Spatial Data Accuracy (NSSDA): “Horizontal accuracy shall be tested by comparing the planimetric coordinates of well-defined points in the dataset with coordinates of the same points from an independent source of higher accuracy.

  3. Vertical Accuracy • Vertical accuracy shall be tested by comparing the elevations in the dataset with elevations of the same points as determined from an independent source of higher accuracy

  4. Well Defined Points • A well-defined point represents a feature for which the horizontal position is known to a high degree of accuracy and position with respect to the geodetic datum. For the purpose of accuracy testing, well-defined points must be easily visible or recoverable on the ground, on the independent source of higher accuracy, and on the product itself.

  5. Comparing & Extracting Elevations • Consistent with best practices, Triangulated Irregular Networks (TINs) of elevation datasets are interpolated at the horizontal coordinates of vertical check points in order to interpolate elevations at those coordinates for the dataset being tested.

  6. Accuracy at 95% • The ASPRS horizontal accuracy standard is based on accuracy classes using root-mean-square-error (RMSE) statistics, whereas the ASPRS vertical accuracy standard is based on accuracy classes using RMSE statistics in non-vegetated terrain, and 95th percentile statistics in vegetated terrain

  7. Taking a Step Back • Root Mean Square Error (RMSE) • Statistical calculation equivalent to the square root of the mean (or average) of the individual errors squared

  8. Meaning… • It is important to understand the difference between the 95%confidence interval and 95thpercentile error • Horizontal Error – 95% Confidence Interval • Assumes normal distribution of the errors • = RMSEr x 1.7308 • Fundamental Vertical Accuracy – 95% C.I. • Assumes normal distribution of the errors • =RMSEz x 1.96

  9. Meaning… • Supplemental Vertical Accuracy is Different • Includes: Urban areas; High grass, weeds, and crops; Brush lands and low trees; Forested, fully covered by trees; Sawgrass; and Mangrove • Assumption is that the data is not normally distributed – there can be biases in the data • SVA is based on 95th percentile error (not C.I.) • Basically, line up absolute value of the errors

  10. Vertical Accuracy… • Statistically, in non-vegetated terrain and elsewhere when elevation errors follow a normal distribution, 68.27% of errors are within one standard deviation (σ) of the mean error, 95.45% of errors are within 2σ of the mean error, and 99.73% of errors are within 3σ of the mean error. The formula 1.96  is used to approximate the maximum error either side of the mean that applies to 95% of the values

  11. ASPRS Draft Accuracy Standards

  12. ASPRS Draft Accuracy Standards

  13. ASPRS Draft Accuracy Standards

  14. 3D Elevation Program • Initiative known as 3DEP • National Enhanced Elevation Assessment (NEEA) was completed in 2011 • NEEA identified significant need for better elevation data • Recommended by the National Digital Elevation Program and its 12 Federal member agencies

  15. 3DEP • Recommended by the National Digital Elevation Program and its 12 Federal member agencies • Endorsed by the National States Geographic Information Council (NSGIC) and the national Geospatial Advisory Committee (NGAC)

  16. Accuracy Levels within 3DEP • Based on Quality Levels • QL1 0.35 m Posting & 9.25 cm RMSEz • 8 ppsm • QL 2 0.7 m Posting & 9.25 cm RMSEz • 2 ppsm • QL 3 1-2 m Posting & <18.5 cm RMSEz • Think of 9.25 cm RMSEz as appropriate for generation of 1 foot contours, with 18.5 appropriate for 2 foot contours

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