Cloud Optimized Point Cloud (COPC) is a file format designed to make lidar or photogrammetry 3d datasets accessible directly over the internet without needing to download the entire file. It is the point cloud equivalent of a Cloud Optimized GeoTIFF (COG). It is essentially a standard LAZ file, but with its data reorganized into a clustered, octree structure. An octree is a hierarchical data structure used to partition three-dimensional space by recursively subdividing it into eight smaller volumes, or “octants.” In the context of lidar and 3d modeling, it is the primary method used to manage massive point clouds, allowing software to efficiently render and query billions of points without overloading system memory. This allows software to stream only the specific points needed for a particular zoom level or geographic area.
Why COPC Files Are More Efficient
The primary benefit of COPC is efficiency. Because the data is structured as an octree, it supports “level of detail” (LoD). If you are zoomed out, the software only pulls a small percentage of points to show a general preview; as you zoom in, it fetches the high-resolution details. This drastically reduces memory usage and bandwidth. Furthermore, since it is backward compatible with LAZ 1.4, any legacy software that doesn’t understand the “cloud optimized” part can still read it as a standard compressed lidar file. It effectively eliminates the need to maintain multiple versions of the same dataset for different applications.
Limitations of COPC Files
While powerful, COPC is not a magic bullet for every scenario. The primary limitation is the processing overhead required to create the file; you must rearrange the points into the octree structure, which can be time-consuming for massive, billion-point datasets. Additionally, while many modern tools support it, some older GIS platforms or specialized engineering software may not yet recognize the spatial indexing, treating it like a bulky, standard LAZ file instead of a streamable resource. Finally, performance is still dependent on your internet connection speed when streaming from remote servers.
How COPC Files Work in the Cloud
You can use COPC files locally or host them on a standard web server (like Amazon S3 or Azure Blob Storage). Because it uses “HTTP Range Requests,” the server doesn’t need any special “point cloud engine” installed; it just needs to serve bits of the file as requested. For developers, libraries like PDAL (Point Data Abstraction Library) are the go-to for creating and manipulating these files. For artists and researchers, web viewers like Potree or Plasio.js can render these clouds directly in a browser.
How to View LiDAR in QGIS
Since version 3.18, QGIS has supported native point cloud viewing, and version 3.32 introduced advanced processing tools that rivaled expensive proprietary software.
Importing:
Simply drag and drop your .las, .laz, or .copc file into the QGIS canvas. If you use a raw .laz file, QGIS will automatically generate a COPC index file next to it to speed up performance.
2D Styling:
In the Layer Styling Panel, you can change the renderer to show points by Elevation, Intensity (how much light bounced back), or Classification (ground, buildings, vegetation).
3D Navigation:
Go to View > New 3D Map View. This opens a dedicated window where you can tilt and rotate your scene.
Note that you can adjust the Point Budget in the layer properties. If your computer is lagging, lower the budget; if you have a powerhouse GPU, crank it up to see millions of points at once.
When working with these datasets, keep a few technical “gotchas” in mind to ensure your 3d modeling is accurate:
Coordinate Reference Systems (CRS): lidar files often lack internal CRS metadata. Always verify the horizontal and vertical datums against your project to avoid “floating” or misplaced data.
Surface Extraction: To move from a “cloud of dots” to a usable map, use the Export to Raster algorithm to create a Digital Elevation Model (DEM) or Digital Surface Model (DSM). From there you can also create hillshades which are ideal for styling cartographic basemaps and interpreting bare earth models for analysis.
