Quick answer: COPC, short for Cloud Optimized Point Cloud, is a LAZ-based format designed for spatial, view-dependent access. Instead of treating a large cloud as one undifferentiated stream of points, a COPC-aware viewer can locate broader or finer spatial nodes as the view changes.
Open WolkeViz Read the COPC viewer guide
Start with LAZ, then add spatial structure
LAS is a binary LiDAR exchange format. LAZ is its losslessly compressed counterpart: the point records still represent coordinates and attributes such as intensity, classification, RGB, GPS time, or extra dimensions, depending on the point format.
COPC builds on that ecosystem. It uses LAZ compression but adds standardized metadata and hierarchy information that tells a client where spatial chunks of the point cloud are stored. The result remains a point cloud with LAS-compatible records, but it can be addressed by region and level of detail.
Why an ordinary LAZ file and COPC can feel different
With a regular LAZ file, a viewer may need to decompress a substantial portion of the file before it can decide which points are useful for the current camera view. For a small file, that is often fine. For a large aerial or mobile-mapping dataset, it can waste work.
COPC organizes the cloud into a hierarchy of spatial nodes. A viewer can begin with a coarse representation, then request or decode more detail for the area that is visible or selected. That pattern is valuable for remote range requests and can also help local interactive viewing.
What “cloud optimized” does and does not promise
Cloud optimized does not mean that every file is instantly fast on every device. The experience still depends on:
- Point density and total file size
- The speed of the storage or network source
- Whether the web server supports byte-range requests for remote files
- GPU memory, browser limits, and the selected point budget
- The attributes stored in each point record
COPC solves the data-organization problem. It does not remove the need to choose a sensible level of detail or to understand the source dataset.
How to inspect a COPC dataset in WolkeViz
Open the file, fit the broad scene, and then move toward the feature you care about. Check the bounds, try RGB, height, intensity, or classification where available, and use measurement or clipping tools for a closer look. That is usually enough to verify whether a delivery contains the expected coverage and attributes before loading it into a full processing workflow.
For a quick first look, open WolkeViz ↗. For related background, see the LAZ viewer guide and the point-cloud measurement guide.