Bachelor Semester Project 6

The goal of this project is to implement a Python script that is able to process LiDAR data stored in LAZ/LAS files. The processing includes clustering of the data points and the visualization of those clusters.

The application was designed to process the LAZ files provided by the luxembourgish data platform available here.

Functionalities

Decompression of LAZ to LAS files
Visualization of the whole LiDAR dataset (or of a specific class of the dataset)
Clustering of the data points belonging to a specific class of the dataset
Choosing out of different clustering algorithms (DBSCAN, KMeans, OPTICS, Agglomerative Clustering and Gaussian Mixture)
Elbow method to choose optimal number of clusters (for KMeans and Agglomerative Clustering)
Input of custom values for the most important parameters of the chosen clustering algorithm
Visualization of the processed (clustered) dataset
Exporting of the processed dataset containing the clustering information in CSV format
Importing of a previously exported dataset (CSV file) in order to visualize it

Installation

There is only one step that has to be performed before being able to run this application/script:

Clone (download) this GitHub project

Pre-requisites

The Python version 3.6 has to be installed on the computer
The following libraries have to be installed :
- matplotlib 3.3.4
- numpy 1.19.5
- pandas 1.1.5
- pptk 0.1.0
- pylas 0.4.3
- scikit_learn 0.24.2
- yellowbrick 1.3.post1

Usage

To run this application on your computer:

Open the terminal
In the terminal, go to the location where the code was cloned (downloaded) (the location of the main.py file)
(If the libraries listed above are not yet installed on your computer, execute the command python3.6 -m pip install -r requirements.txt to install the libraries for Python 3.6)
Execute the command python3.6 main.py

Screenshots