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

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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

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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

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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