I recently published a practical guide to PDAL pipelines in Python for point cloud workflows.

It covers:

- PDAL pipeline structure
- running pipelines from Python
- returning results as NumPy arrays
- clipping and merging point clouds
- creating a GeoTIFF DEM from a LAZ file

The focus was on practical examples for real processing tasks.

https://medium.com/@anton.biatov/pdal-in-python-a-practical-guide-to-point-cloud-pipelines-1f7e2bc3e2ea

Also interested in what tools people use for point cloud work in production or research.

#PDAL #LiDAR #Python #GIS #geospatial

Week 1: The long game 🗺️

7 projects, 2011-2022. OpenLayers → #LiDAR → Landsat → #ThreeJS Antarctica.

Tools evolved dramatically. #WebGL existed (Mapbox) but wasn't customizable. #PDAL hadn't opened LiDAR beyond enterprise. Shaders weren't on radar. But question stayed: how make terrain feel real online?

Answers accumulated slowly, project by project.

Week 2: GIS foundations → interactive data viz. Acceleration begins ⚡

#100DayMapChallenge Days 1-7

Day 7/100: Mapping a restless volcano 🌋

Taal Volcano, Philippines - one of the world's most active volcanic systems. Caldera inside a lake formed by earlier eruptions. #PDAL #Entwine #Potree workflow on 172M points from PHIL-LiDAR Program.

Volcanic LiDAR as temporal document: spatial data is never static. Terrain evolves.
Context isn't recreation - it's about understanding landscape that may change without warning.

Week 1 complete! GIS foundations → D3.js phase next.

#100DayMapChallenge

Day 5: Point clouds cross borders 🌍

In 2015, NYU captured one of the densest public #LiDAR datasets: Dublin at sub-meter resolution. Years later, I processed 500M+ points using #PDAL and #Potree, applying the same pipeline from Yosemite.

NYC captures. Romania processes. Anyone explores.
Geography is local. Tools are global.

https://blog.maptheclouds.com/learning/lidar-pdal-experiments-dublin

#100DayMapChallenge

Day 3/100: 25M points in one file. Where to start?

FOSS4G 2019: Connor Manning & Adam Steer demo'd PDAL/Entwine. Hooked instantly.

Sequence matters: classify, filter, mesh, interpolate.
Filter too early → lose detail. Too late → noise corrupts.

570M points. 19h processing. Open-source JSON pipelines. All documented. Visualization: seconds. Processing: 19 hours.

https://blog.maptheclouds.com/learning/lidar-pdal-experiments-yosemite-valley

#PDAL #LiDAR #PointCloud #FOSS4G #GIS #100DayMapChallenge

I was running out of RAM and out of swap while trying to generate a copc out of a laz with #pdal (the laz weighs 4.7GB). So I created a 15GB swapfile by simply adding

swapDevices = [{
device = "/var/lib/swapfile_15G";
size = 15*1024;
}];

to configuration.nix, rebuild, and voilà. The process is currently running very smoothly, peak consuming the whole RAM as expected (64GB) + 12GB out of the available 24GB.

cc @sguimmara @autra

#linux #nixos #pointcloud #lidar

🚀 Today, I'm running a workshop on #PointCloud Processing in #QGIS at #FOSS4GEurope 2025 in #Mostar! We'll dive into:
🔹️ Downloading & preprocessing data
🔹️ Creating DSMs with interpolation
🔹️ 3D visualization & styling
🔹️ Elevation profiles & filtering
🔹️ Automation with #PDAL Wrench
🔹️ Editing point clouds

💻 Can’t join in person? Free access at @gisocw
🔗https://courses.gisopencourseware.org/

Celebrate Open Education Week 2025!🎉

Today's highlight from GIS OpenCourseWare: Point Cloud Processing with QGIS & PDAL Wrench. Learn to work with point cloud data and automate workflows for faster, efficient processing. 🚀 #OpenEducationWeek #OEWeek2025 #GIS #QGIS #PointCloud #PDAL