
The industry-standard Swiss Army Knife for point cloud data translation and manipulation.
PDAL is a C++ library and command-line application suite designed for the translation and manipulation of point cloud data. Often referred to as the 'GDAL for point clouds,' PDAL provides a robust architecture for orchestrating complex geospatial workflows. In the 2026 market, it stands as the foundational middleware for AI-driven environmental modeling and autonomous vehicle perception pipelines. Its core strength lies in its 'Pipeline' architecture, where JSON-based workflow definitions allow users to chain together readers, filters, and writers into reproducible data processing units. PDAL supports a vast array of formats including LAS/LAZ, EPT, BPF, and Oracle OCIPointCloud, while offering sophisticated filtering capabilities such as ground classification, noise removal, and reprojection. For AI Solutions Architects, PDAL is the critical pre-processing layer that cleanses and structures raw sensor data before it is ingested by 3D deep learning models like PointNet++ or KPConv. It is maintained by a global community of geospatial experts and is released under the permissive BSD license, ensuring it remains a staple in both commercial and research-driven spatial computing stacks.
Workflows are defined in a declarative JSON format, allowing for complex, multi-stage processing graphs that are easily versioned and shared.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Native support for reading and writing EPT format, which is an octree-based format designed for streaming massive point clouds over HTTP.
Allows for custom logic to be injected into a pipeline using NumPy via the 'filters.python' stage.
Implements SMRF (Simple Morphological Filter) and PMF (Progressive Morphological Filter) for high-accuracy terrain modeling.
Access to every standard and custom point dimension (XYZ, Intensity, ReturnNumber, etc.) during processing.
A dynamic loading system allows users to add new readers/writers (like Oracle or Nitf) without recompiling the core library.
Deep integration with GDAL for spatial reference systems and raster-to-point operations.
Raw LiDAR scans come in various coordinate systems and formats, making them unusable for unified city planning.
Registry Updated:2/7/2026
Vegetation and buildings must be removed from LiDAR data to create Digital Terrain Models (DTMs).
Massive point clouds are too dense for neural network training and require subsampling.