Accuracy and Maps Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science.
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Transcript of Accuracy and Maps Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science.
![Page 1: Accuracy and Maps Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science.](https://reader036.fdocuments.in/reader036/viewer/2022062518/56649e7b5503460f94b7be94/html5/thumbnails/1.jpg)
Accuracy and Maps
Mike Ritchie, PE, PLS, PSM, CPPresident and CEO
Photo Science
![Page 2: Accuracy and Maps Mike Ritchie, PE, PLS, PSM, CP President and CEO Photo Science.](https://reader036.fdocuments.in/reader036/viewer/2022062518/56649e7b5503460f94b7be94/html5/thumbnails/2.jpg)
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.
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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
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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.
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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.
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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
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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
Reported Measured Delta Delta2
100 101 -1 1150 149 1 1170 172 -2 4
Average = 2RMSE = 1.41
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Meaning…
• It is important to understand the difference between the 95% confidence interval and 95th percentile 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
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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
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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
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ASPRS Draft Accuracy Standards
Vertical Data Accuracy Class
RMSEz in Non-
Vegetated Terrain
(cm)
Non-Vegetated Vertical Accuracy
(NVA) at 95% Confidence Level
(cm)
Vegetated Vertical
Accuracy (VVA) at 95th Percentile
(cm)
Lidar Relative Accuracy Swath-to-
Swath in Non-Vegetated Terrain (RMSDz/Max Diff)
(cm)
I 1.0 2.0 3.0 0.8/1.6
II 2.5 5.0 7.5 2.0/4.0
III 5.0 10.0 15.0 4.0/8.0
IV 10.0 20.0 30.0 8.0/16.0
V 12.5 25.0 37.5 10.0/20.0
VI 20.0 40.0 60.0 16.0/32.0
VII 33.3 66.7 100.0 26.7/53.3
VIII 66.7 133.3 200.0 53.3/106.6
IX 100.0 200.0 300.0 80.0/160.0
X 333.3 666.7 1000.0 266.6/533.4
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ASPRS Draft Accuracy Standards
Vertical Data Accuracy Class
RMSEz in Non-Vegetated
Terrain(cm)
Non-Vegetated Vertical Accuracy (NVA) at 95% Confidence Level (cm)
Vegetated Vertical Accuracy (VVA) at 95th
Percentile (cm)
Appropriate Contour Interval supported by
the RMSEz value
Recommended Minimum Nominal
Pulse Density (pts/m2)/ Maximum Nominal
Pulse Spacing (meters)
I 1.0 2.0 3.0 3 cm ≥20/0.224II 2.5 5.0 7.5 7.5 cm 16/0.250III 5.0 10.0 15.0 15 cm (~6”) 8/0.354IV 10.0 20.0 30.0 30 cm (~1’) 2/0.707V 12.5 25.0 37.5 37.5 cm 1/1.000VI 20.0 40.0 60.0 60 cm (~2’) 0.5/1.414VII 33.3 66.7 100.0 1-meter 0.25/2.000VIII 66.7 133.3 200.0 2-meter 0.1/3.162IX 100.0 200.0 300.0 3-meter 0.05/4.472X 333.3 666.7 1000.0 10-meter 0.01/10.000
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ASPRS Draft Accuracy Standards
Project Area (Sq. Miles)
Horizontal Testing Vertical Testing (not clearly-defined points)
Total Number of Static Horizontal Check Points (clearly-defined points)
Number of Static Vertical Check Points
in NVA
Number of Static Vertical Check Points
in VVA
Total Number of Static Vertical Check
Points
≤ 200 20 20 0 20201 – 300 25 20 10 30301 – 400 30 25 15 40401 – 500 35 30 20 50501 – 600 40 35 25 60601 – 700 45 40 30 70701 – 800 50 45 35 80801 – 900 55 50 40 90
901 – 1000 60 55 45 100
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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
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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)
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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