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LiDAR data processing using Python

Written by Jinha Jung (jinha@purdue.edu), Sungchan Oh (oh231@purdue.edu), and Yuri Kim (yurikim@iu.edu). Please contact authors for any questions on this tutorial.

Pre-requisite

You need Git installed on your machine. Please follow instruction from https://git-scm.com/book/en/v2/Getting-Started-Installing-Git to install Git on your machine.

Once the Git is installed on your machine, clone this repository on your local machine.

$ cd [YOUR_TARGET_DIRECTORY]
$ git clone https://github.com/gdslab/tutorial_lidar_processing_with_python.git

LiDAR 101 (10 min) - Yuri Kim

  1. What is LiDAR?

Installing Conda/Anaconda and configuring Virtual Environments (40 min) - Jinha Jung

Click here for more detail.

  1. Anaconda installation on Windows
  2. Anaconda installation on Mac
  3. Anaconda installation on Linux
  4. Why do we need Virtual Environment?
  5. Setting up a Virtual Environment for LiDAR data processing

LiDAR file I/O using laspy (30 min) - Jinha Jung

Click here for more detail.

  1. Reading and writing las/laz file
  2. Converting laz to las format
  3. Converting las version
  4. Visualizing las file using Cloud Compare

Generating raster data from LiDAR (70 min) - Jinha Jung

Click here for more detail.

  1. Generating Digital Surface Model (DSM) from a las file
  2. Generating Normalized Digital Height Model (NDHM) from a las fil

Application of DSM, DTM, and NDHM (30 min) - Yuri Kim

  1. 3D-visualizing DSM, DTM and NDHM using QGIS
  2. Exploring and Visualizing LiDAR with contour, classification, etc.

Things to prepare before the workshop

  1. A computer or laptop (Windows/Mac/Linux)
  2. HDD storage space over 2GB
  3. Reliable internet connection
  4. Install QGIS LTS version
  5. Basic familiarity with Python