2025-07-24 –, Main Room 1 (Main stage)
A growing number of countries are using airborne lidar scanning to collect geospatial point-cloud data at sub-meter-scale resolution for their whole territory and making it freely available. PointClouds.jl allows you to make use of such datasets by locating available data for your coordinates of interest, downloading and reading the data in the specialized LAS format, and extracting useful information from the raw point cloud through a series of processing steps.
To analyze and model the physical world with computational tools, we need a digital representation – sometimes known as “digital twin” – of the environment. Obtaining detailed information such as the location and shape of buildings and vegetation can however be a major challenge.
Airborne lidar data is collected with aircraft-mounted laser scanners that measure the location of millions of points on the land surface. A growing number of countries are creating such scans of their whole territory and make the data freely available online. There is a good chance that you can download high-resolution point-cloud data (often tens of points per m²) for the location of your home, your scientific study, or your engineering project.
However, that data is in the form of an unstructured “point cloud” consisting of 3D coordinates and some additional metadata. To extract useful information generally requires a series of processing steps such as coordinate transforms, filtering, classification, and rasterization. This processing might require interactive experimentation with project-specific code that runs fast enough to handle millions of points – a task that Julia is uniquely suited for.
With PointClouds.jl we present a new package for working with point-cloud data. It can query available datasets to find and download available data for your location of interest and read the data in the specialized LAS format and its compressed LAZ variant. It also implements common processing steps with support for multithreading and lazy processing of data that does not fit into memory, allowing you to build a processing pipeline tailored to your specific needs.
In our research work, we use PointClouds.jl to model wind flow in urban environments. The point-cloud data allows us to include detailed representations of the local terrain, buildings, and vegetation in our computational flow models to generate more accurate predictions for applications in air quality, urban climate, wind engineering, and unmanned aerial vehicles.
Postdoctoral researcher at the Environmental Flow Physics Lab at Columbia University, @mfsch on GitHub.
Marco is Assistant Professor in the CEEM Department at Columbia University and an Amazon Visiting Academic.
He received his PhD in Civil and Environmental Engineering from Braunschweig and Florence Universities (2014), and a PhD in Mechanical Engineering from École Polytechnique Fédérale de Lausanne (2016). Prior to joining Columbia, he held postdoctoral positions at the University of British Columbia and at the Center for Turbulence Research at Stanford University.