2026-08-31 –, 608
A hands-on introduction to open-source LiDAR workflows, from raw point clouds to terrain products, feature extraction, classification, and 3D visualization with open-source tools.
The workshop begins with the ASPRS standard, introducing essential concepts such as point classifications, return numbers, and other fundamental attributes that define LiDAR data. Participants will learn how to interpret these attributes correctly, assess data quality, and use them for more informed analysis.
Moving beyond the basics, the workshop explores open-source workflows with PDAL, GRASS, and WhiteboxTools for data reading, manipulation, and spatial processing, together with CloudCompare, QGIS and pybabylonjs for dynamic 3D visualization. TileDB will also be presented as a robust storage option for managing point clouds as large dataframes. Along the way, participants will work through practical tasks such as gridding, interpolation, vectorization, and classification, highlighting reproducible approaches for handling and visualizing large-scale point clouds.
By integrating Python scripts with these specialized tools, participants will see how complex processing chains can be automated and turned into compelling visual outputs. Basic Python knowledge is helpful but not required, and sample code will be provided. By the end of the workshop, participants will understand the core concepts behind LiDAR data and gain practical skills for processing and presenting it in effective and visually engaging ways.
A personal laptop with VSCodium installed, including notebook support, plus Conda, Miniconda, Mamba, or Micromamba for setting up the workshop environment. Docker is optional for participants who prefer to run the workshop in a container, but it should be installed in advance with the necessary user permissions. CloudCompare should also be installed. The workshop environment will include GRASS, QGIS, and the rest of the required software stack. All required data will be provided, and setup instructions will be carefully prepared and shared well in advance.
What skills do participants require to have?:Basic GIS knowledge is expected. Basic familiarity with point clouds or LiDAR data is helpful but not required. Some exposure to Python is useful, but participants are not expected to code from scratch, since prepared materials and example workflows will be provided.
Link to software source code:
I work at the Geodetic Institute of Slovenia in Ljubljana as Head of Digital Transformation and contribute to projects as a Senior Geospatial Data Scientist and Remote Sensing Specialist. My work focuses primarily around the analysis of multispectral, hyperspectral, and SAR imagery, as well as LiDAR point clouds, though I enjoy tackling data problems of all kinds. I rely heavily on Python, GRASS, GDAL, PDAL, QGIS and PostgreSQL for data torturing and distribution. I love Linux. I currently serve as the secretary of the Slovenian OSGeo Local Chapter.
I’ve been building GIS solutions at the Geodetic Institute of Slovenia for over 15 years, working across the stack on everything from web mapping applications to data processing pipelines. My background is in biomedical engineering, but I found my way into geospatial tech through the field of automation — and I’ve been streamlining processes and visualizing data ever since.