PDAL: An open source library for the processing and analysis of point clouds

As large point cloud datasets become ubiquitous in the Earth science community, open source libraries and software dedicated to manipulating these data are valuable tools for geospatial scientists and practitioners. We highlight an open source library called the Point Data Abstraction Library, more commonly referred to by its acronym: PDAL. PDAL provides a standalone application for point cloud processing, a C++ library for development of new point cloud applications, and support for Python, MATLAB, Julia, and Java languages. Central to PDAL are the concepts of stages, which implement core capabilities for reading, writing, and filtering point cloud data, and pipelines, which are end-to-end workflows composed of sequential stages for transforming point clouds. We review the motivation for PDAL’s genesis, describe its general structure and functionality, detail several options for conveniently accessing PDAL’s functionality, and provide an example that uses PDAL’s Python extension to estimate earthquake surface deformation from pre- and post-event airborne laser scanning point cloud data using an iterative closest point algorithm. •PDAL is a format-agnostic, open-source library for processing point cloud data.•Provides format translation, geospatial transformations, and point cloud filtering.•Distinguished by a pipeline syntax for point cloud read, filter, and write tasks.•Freely accessible to C++ developers and Python, Java, MATLAB, and Julia users.