LiDAR Segmentation

Sets of python scripts for lidar data processing with batsh processing

Python Dependencies

* pdal
* json 

Main processing

1. pdal_merge.py

   • Merge multiple las files and handle invalid ReturnNumber/NumberOfReturns into a single (las/laz) file
   • python pdal_merge.py <mergedfile>
   • Example python pdal_merge.py merged_lidar.laz

2. pdal_segmentation.py

   • Generate segmented point coulds as buildings and trees (laz/las)
   • python pdal_segmentation.py <infile> <filtertype> <segmented_pts>
   • Example python pdal_segmentation.py merged_lidar_10percent.laz csf segmented_pts.laz

3. buildings_extraction.py

   • Extract point clouds classified as Buildings (laz/las)
   • python buildings_extraction.py <infile> <filtertype> <extract_Buildings>
   • Example python buildings_extraction.py merged_lidar_10percent.laz csf extract_Buildings.laz

Extra processing

1. pdal_pipeline.py

   • Generate a pdal pipeline able to output segmented Gound-Nongound point clouds las/las, DTM and DSM (tif)
   • python pdal_pipeline.py <infile> <filtertype> <Gr_NGr_pts> <dtm> <dsm>
   • Example python pdal_pipeline.py merged_lidar_10percent.laz csf Gr_NGr_csf.laz dtm_pdal.tif dsm_pdal.tif

2. pdal_FirstReturns

   • Generate first returns point clouds (laz/las)
   • python pdal_FirstReturns.py <filtertype> <first_returns>
   • Example python pdal_FirstReturns.py csf first_returns.laz

lidR capabilities

lidR_funcrtions include different functions for point clouds manipulation, vizualisation and an automated processing chain with lidR

Author

• Walid Ghariani - MSc. Student Applied Earth Observation and Geoanalysis (EAGLE) linkedin E-mail: [email protected]