Version 4 (modified by knpa, 14 years ago) (diff)

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LIDAR data delivery

Once the data has been processed, it needs to be put into a delivery directory. This is the final file structure in which it will be sent to the customer.

What should be included

  1. ASCII LIDAR pointcloud data
  2. pdf version of flight logsheet
  3. readme file describing the data set + copyright notice
  4. screenshot of mosaic of all lines (full resolution) and zoomed image with vector overlay
  5. data quality report and further processing scripts
  6. DEM of the LIDAR data - usually gridded to 2m resolution.
  7. Screenshot of DEM

Procedure for creating a LIDAR data set delivery

Semi-scripted method

  1. Create the delivery directory using make_lidar_delivery.sh <full path to main project dir> <year> <proj-code> <julian_day>. The .LAS files that have been QC'ed and classified for noisy points should be in the main project directory under leica/proc_laser/ for this script. Check it's created everything correctly. If it fails, create the directory manually as per below.
  2. If it is a UK project then dem and screenshots will be created. A screenshot of the intensity image with vectors overlaid will be created if there are vectors in ~arsf/vectors/from_os/PROJ_CODE. If the project is outside the UK then this script will not generate the dem or screenshots so these need to be created manually using make_lidardem_or_intensity.sh.
  3. Generate the readme file.
    1. Create a config file for the read me using the generate_readme_config.py script. Use a command such as generate_readme_config.py -d <delivery_directory> -r lidar
    2. Edit the config file and check all the items are filled in:
      • Any remarks about the data should be entered as a sentence in the "data_quality_remarks" section.
      • If vectors have been used then the accuracy should be entered in "vectors" (e.g. '5-10' if they're within 5m to 10m)
      • line_numbering should contain a space separated list of line names linking the logsheet to the processed files.
      • las_files should contain the path to the processed LAS file in order to extract the per flightline statistics
      • elevation_difference should contain a list of the elevation differences between overlapping flightlines. Enter the lines numbers and the difference in cm followed by a semicolon e.g 001 002 5; 002 003 4.5; etc...
      • All "compulsory" items should contain data
    3. Create a TeX file. Use the script create_latex_lidar_readme.py -f <config filename>
      1. This file can be reviewed and edited in any text editor if needed
    4. Create a PDF file by running latex <tex_filename>
      1. Review the read me and check carefully to see if it looks OK with all relevant information present
      2. Copy it to the delivery directory and remove any temporary files. Recommended to keep the TeX file until after delivery checking in case any edits are required

Manual Method

  1. Copy the template directory over to the project directory. Template directory at ~arsf/arsf_data/2009/delivery/lidar/template
  2. Convert the LAS binary files into ASCII files, ensuring to output all the appropriate information
    • run las2txt.sh <lasdirectory>
    • OR run las2txt --parse txyzicra <lidarfilename> <outputfilename> for each file, outputting to the ascii_laser directory (may not work with LAS 1.1).
    • REMEMBER THAT THESE LAS FILES SHOULD HAVE BEEN QC'ED AND CLASSIFIED FOR NOISY POINTS
    • If not already done, rename the files in the convention "LDR-PPPP_PP-yyyydddfnn.txt" (details in readme).
  3. You need to create a DEM from the lidar data to include with the delivery. Use make_lidardem_or_intensity.sh and put the output file in a directory named 'dem'. Noisy points (those with classification 7) should not be included in the DEM so remember to specify the -C option.
  4. Include a pdf version of the flight logsheet with the delivery
  5. Make sure correct upto date data quality report (pdf version) is included in docs directory
  6. Create full resolution JPGs of mosaic of all the LIDAR lines by intensity, a separate one of the intensity with vectors overlaid (if vectors are available) and one of the dem and put in screenshot directory (with make_lidardem_or_intensity.sh?).
  7. Generate the readme using as per point 3 above

If you have hyperspectral data to make into a delivery, go to the hyperspectral delivery page.

If not, or if you've done that already, the delivery is ready for checking.