Explore & Process 3D Point-Cloud Data
PointClouds.jl lets you query publicly available lidar point-cloud datasets, download the data for regions of interest, and explore & process the data locally. You can access all attributes stored in LAS/LAZ files and construct purpose-built processing pipelines that extract information about the local environment from point-cloud data.
Main features & goals
PointClouds.jl aims to provide all functionality for an ergonomic, high-performance workflow to acquire and process point-cloud data. The initial development is focused on producing robust primitives for such a workflow. Over time, we expect the functionality to expand, building out a comprehensive library of point-cloud processing algorithms similar to PDAL and lidR.
- spatial queries of the USGS 3DEP lidar data with near-total coverage of the United States
- reading & writing LAS/LAZ files (v1.0 – v1.4), including files that do not fit into memory
- lazy filtering & updating of LAS/LAZ points
- parsing LAS coordinate reference system (CRS) data in WKT & GeoTIFF formats; coordinate transforms
- multi-threaded in-memory processing of point-cloud data, including neighborhood-based processing
- rasterization of point-cloud data based on points within pixel footprint, points within radius of pixel center, and k nearest neighbors of pixel center
For more details, see the table of contents below.
Use cases
PointClouds.jl is primarily designed to work with geospatial point-cloud data obtained from airborne lidar scans. The typical use cases consist of loading such data from LAS/LAZ files and performing a series of processing steps such as filtering, classification, and rasterization to extract useful information from the raw point-cloud data.
A particular focus is given to these three use cases:
- LAS/LAZ file manipulation: As a “Swiss army knife” for LAS/LAZ data, PointClouds.jl aims to adhere strictly to the specifications, make every byte available for reading and writing, and enable reading/writing larger-than-memory data in a streaming manner.
- Developing algorithms for point-cloud processing: Thanks to its “just-ahead-of-time” compilation model, Julia is an excellent environment for iterative exploratory development that is performance-sensitive, as is the case when working with millions of points. PointClouds.jl provides primitives to quickly try out ideas for new point-cloud-processing algorithms and apply them to data for various locations.
- Lidar-based analyses for arbitrary locations: Through the integration with national databases of lidar data, processing pipelines built on PointClouds.jl can be applied to new locations with ease.
Related packages
The LAS/LAZ file support in PointClouds.jl was inspired by LasIO.jl and LazIO.jl. While these are less complete in some areas such as CRS and LAS 1.4 support (as of May 2025), they have been stable for many years and can still be a good option if you are looking for a thin layer on top of the raw LAS/LAZ data. The LASDatasets.jl package is another more recent option for loading/writing LAS data that has more complete support for storing custom data in LAS files. The specific use case of rasterizing larger-than-memory LAS files is covered by PointCloudRasterizers.jl and not currently covered by PointClouds.jl, which in turn has more functionality for in-memory rasterization.
There are of course many other Julia packages that can be useful when working with point-cloud data, even when this is not their primary goal. The JuliaGeo and JuliaEarth communities have built out a lot of functionality for working with geospatial data, so their work can be a good starting point before exploring the broader package ecosystem.
Installation
PointClouds.jl is available in the official Julia package registry. Add it to a Julia environment using e.g. the Pkg REPL:
(@v1.10) pkg> add PointCloudsFollow the tutorial for an introduction to the functionality of PointClouds.jl.
Since PointClouds.jl is rather new and under active development, backwards-incompatible changes may occur regularly. It is therefore recommended to add a specific version to your Julia environment (e.g. with Pkg.compat or Pkg.pin and to be careful when updating to new (non-patch) versions.