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pythonlidar-com/README.md

Python LiDAR & Point Cloud Workflows — PDAL pipelines and point cloud processing in Python

Python LiDAR & Point Cloud Workflows

Code-first, production-grade guides for processing LiDAR and point clouds in Python.
PDAL pipelines · ground filtering · DTM/DSM generation · batch & cloud automation.

🌐 Live site — www.pythonlidar.com


What is this?

pythonlidar.com is a reference library for engineers who process airborne, terrestrial, and mobile LiDAR with Python. Every guide is built around runnable code — real PDAL pipeline JSON, typed Python functions with logging and error handling, realistic EPSG codes and parameter values — not editorial fluff. If you work with .las/.laz files, PDAL, GDAL, or laspy and need to ship reproducible workflows, this is written for you.

It is a fast, fully static site (no trackers, offline-capable) covering the whole practitioner workflow: ingest → validate → filter → classify → rasterize → automate.

Who it's for

  • LiDAR analysts turning raw scans into deliverables
  • Python GIS developers building reproducible geospatial pipelines
  • Surveying & mapping tech teams standardizing point cloud processing
  • Infrastructure & urban-planning engineers generating terrain models at scale

What it covers

The material is organized into four focused tracks:

Track What you'll learn
PDAL Pipelines Pipeline architecture and the streaming execution model, stage chaining, filtering logic, spatial reprojection, attribute mapping, parallel execution, memory management, and pipeline validation.
Point Cloud Standards LAS/LAZ binary structure, ASPRS classification codes, coordinate reference systems, point density metrics, and metadata/header integrity.
Ground & Terrain Models SMRF and PMF ground classification, DTM and DSM raster generation with writers.gdal, interpolation choices, NoData void filling, and hillshade/slope/aspect derivation.
Batch & Cloud Automation Containerized PDAL with Docker, AWS Batch tile fan-out, streaming LAZ and Cloud-Optimized GeoTIFF I/O against Amazon S3, and Apache Airflow DAG orchestration.

Each guide includes a hand-authored diagram, a copy-paste-ready working example, parameter reference tables, verification steps, and a troubleshooting section for the errors you actually hit in production.

Highlights

  • Runnable PDAL & Python — every pipeline JSON is pdal pipeline --validate-clean, every script is self-contained.
  • Real parameters — actual EPSG codes, typical SMRF slope/window values, real LAS dimension names.
  • Accessible & fast — WCAG 2 AA, mobile-first, Lighthouse-budgeted, works offline.
  • Structured dataArticle, HowTo, FAQPage, and BreadcrumbList on every page.

Tech stack

npm install      # install dependencies
npm run build    # build to _site/
npm run serve    # local dev server on :8080

Links


Built for LiDAR analysts, Python GIS developers, and surveying teams.

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