using fme to automate lidar qa\qc processes
Post on 10-Feb-2017
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Lidar Quality Checks via FME Workbenches
Geospatial Data ServicesManitoba Hydro
Geospatial Data Services
Support Manitoba Hydro’s operational activities … through the application of spatial data processing and reporting tools.
About Us
Technical Assistant Technical Assistant Rock Star(Geomatics Engineer)
John Huillery Justin Avery Arch Csupak
Lidar Data Acquisition:GDS is responsible for coordinating the lidar requirements of the Corporation.-Includes Lidar and DOI for new and existing transmission corridors and wide area mapping
Data Volume:Over the last few years we have contracted the data collection for 3000 km of corridor studies and 1000 sq. km of wide area mapping.This translates to about 5 TB of data which we have to perform QC and acceptance testing on.
QC and Acceptance:We have fairly detailed specifications for many deliverables with specific criteria including formats, projections, and legacy vertical datums.
Therefore we needed a way to automate the process.
Lidar Quality ChecksAmongst several other checks, FME is used to:-Perform initial checks for classification errors.-Determine how new lidar data compares vertically to previous lidar missions.
Initial Classification Checks
LAS Format 1.4 Specified for 2015 Lidar Collection
Allows for extended classification scheme
LAS 1.4 format includes the use of classification flags:
• Key Point (thinned data for modeling purposes)• Overlap (adjacent flight lines)
• Synthetic (points added after collection)• Withheld (points that contain errors)
Class Flags are supplied as codes 0 to 15, and can consist of combinations of multiple class flags.
How do Key Points Help?
Can now be identified/treated as rock outcrop, paved road, etc. within a given
analysis or model.
Classification flags also introduce the possibility of error.
FME is used to identify initial class / classification flag errors.
Workbench Part 1Due to LAS file sizes and memory limits, 1 tile is processed at a time through a Workspace Runner transformer.
Workbench Part 1‘Prompt and Run’
One Input LAS Folder Location
One Output File LAS file summaries and errors
Pressing ‘OK’ starts second workbench as a sub-routine, which processes each tile in turn.
Workbench Part 2LAS Checks:• Determine Horiz.+Vert. Coord. System and LAS version
• Test for illegal class/class flag combinations (ie. Key Point in vegetation, Key Point and Withheld flag existing on same point, etc.)
• Summarize findings in MS Excel Workbook:• One sheet for error summary• One sheet summarizes LAS version, Horz / Vert Coord. System• One sheet per tile (contains detailed summary of all points)
Workbench Part 2
Results
Results
Results
Class 13 = Wire - Guard
Vertical Datum Validation
• Validate vertical datum with respect to previous datasets
Vertical Datum Validation
• Validate vertical datum with respect to previous datasets• Verify data conforms to specifications
Vertical Datum Validation
• Validate vertical datum with respect to previous datasets• Verify data conforms to specifications• Easily identify elevation anomalies
Workflow - Vertical Datum Validation
Workflow - Vertical Datum Validation
FME
Identify Intersecting LiDAR Coverage
Known Vertical Datum
Unknown Vertical Datum
Z-Delta Calculator Workbench
Z-Delta Calculator WorkbenchCreate point grid of AOI(s)
Generate TIN from LiDAR (both)
Extract elevation values to point grids and calculate difference
Calculate Statistics
AOI Summary Stats
Results Excel Workbook
Results Feature Class
Create Point Grid of Area(s) of Interest
Generate TINs from LiDAR
Extract z-Delta Values to Point Grid
Extract z-Delta Values to Point Grid
Calculate Descriptive Statistics
Thank you!
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