COPC – Cloud Optimized Point Cloud

Range-readable, compressed, geospatial organized LAZ specification

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Cloud Optimized Point Cloud Specification – 1.0

©2021, Hobu, Inc. All rights reserved.


Table of contents

  1. Version
  2. Introduction
  3. Notation
  4. Implementation
    1. LAS PDRF 6, 7, or 8
    2. info VLR
    3. hierarchy VLR
  5. Differences from EPT
  6. Example Data
  7. Reader Implementation Notes
  8. Credits
  9. Pronunciation
  10. Discussion
  11. Structural Changes to Draft Specification
  12. COPC Software Implementations
  13. Validation


This document defines Cloud Optimized Point Cloud (COPC) version 1.0.

This document is available as a PDF at copc-specification-1.0.pdf.


A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describe the octree organization of data that are stored in LAZ 1.4 chunks.

``info`` VLR and the LAZ chunk table allow COPC readers to select and seek through the file.

Data organization of COPC is modeled after the EPT data format, but COPC clusters the storage of the octree as variably-chunked LAZ data in a single file. This allows the data to be consumed sequentially by any reader than can handle variably-chunked LAZ 1.4 (LASzip, for example), or as a spatial subset for readers that interpret the COPC hierarchy. More information about the differences between EPT data and COPC can be found below.


Some of the file format is described using C-language fixed width integer types. Groups of entities are denoted with a C-language struct, though all data is packed on byte boundaries and encoded as little-endian values, which may not be the case for a C program that uses the same notation.


Key aspects distinguish an organized COPC LAZ file from an LAZ 1.4 that is unorganized:

LAS PDRFs 6, 7, or 8

COPC files MUST contain data with ONLY ASPRS LAS Point Data Record Format 6, 7, or 8. See the ASPRS LAS specification for details.

info VLR

User ID Record ID
copc 1

The info VLR MUST exist.

The info VLR MUST be the first VLR in the file (must begin at offset 375 from the beginning of the file).

The info VLR is 160 bytes described by the following structure. reserved elements MUST be set to 0.

struct CopcInfo

  // Actual (unscaled) X coordinate of center of octree
  double center_x;

  // Actual (unscaled) Y coordinate of center of octree
  double center_y;

  // Actual (unscaled) Z coordinate of center of octree
  double center_z;

  // Perpendicular distance from the center to any side of the root node.
  double halfsize;

  // Space between points at the root node.
  // This value is halved at each octree level
  double spacing;

  // File offset to the first hierarchy page
  uint64_t root_hier_offset;

  // Size of the first hierarchy page in bytes
  uint64_t root_hier_size;

  // Minimum of GPSTime
  double gpstime_minimum;

  // Maximum of GPSTime
  double gpstime_maximum;

  // Must be 0
  uint64_t reserved[11];

hierarchy VLR

User ID Record ID
copc 1000

The hierarchy VLR MUST exist.

Like EPT, COPC stores hierarchy information to allow a reader to locate points that are in a particular octree node. Also like EPT, the hierarchy MAY be arranged in a tree of pages, but SHALL always consist of at least ONE hierarchy page.

The VLR data consists of one or more hierarchy pages. Each hierarchy data page is written as follows:

The VoxelKey corresponds to the naming of EPT data files.

struct VoxelKey
  // A value < 0 indicates an invalid VoxelKey
  int32_t level;
  int32_t x;
  int32_t y;
  int32_t z;

An entry corresponds to a single key/value pair in an EPT hierarchy, but contains additional information to allow direct access and decoding of the corresponding point data.

struct Entry
  // EPT key of the data to which this entry corresponds
  VoxelKey key;

  // Absolute offset to the data chunk if the pointCount > 0.
  // Absolute offset to a child hierarchy page if the pointCount is -1.
  // 0 if the pointCount is 0.
  uint64_t offset;

  // Size of the data chunk in bytes (compressed size) if the pointCount > 0.
  // Size of the hierarchy page if the pointCount is -1.
  // 0 if the pointCount is 0.
  int32_t byteSize;

  // If > 0, represents the number of points in the data chunk.
  // If -1, indicates the information for this octree node is found in another hierarchy page.
  // If 0, no point data exists for this key, though may exist for child entries.
  int32_t pointCount;

The entries of a hierarchy page are consecutive. The number of entries in a page can be determined by taking the size of the page (contained in the parent page as Entry::byteSize or in the COPC info VLR as CopcData::root_hier_size) and dividing by the size of an Entry (32 bytes).

struct Page
    Entry entries[page_size / 32];

Differences from EPT

Example Data

Reader Implementation Notes

COPC is designed so that a reader needs to know little about the structure of a LAZ file. By reading the first 589 bytes (375 for the header + 54 for the COPC VLR header + 160 for the COPC VLR), the software can verify that the file is a COPC file and determine the point data record format and point data record length, both of which are necessary to create a LAZ decompressor.

Readers should:

The octree hierarchy is arranged in pages. The COPC VLR provides information describing the location and size of root hierarchy page. The root hierarchy page can be used to traverse to child pages. Each entry in a hierarchy page either refers to a child hierarchy page, octree node data chunk, or an empty octree node. The size and file offset of each data chunk is provided in the hierarchy entries, allowing the chunks to be directly read for decoding.

Server Implementation Notes

For streaming of COPC files from HTTP servers to work, the server hosting COPC files needs to be configured to allow:


COPC was designed in July–November 2021 by Andrew Bell, Howard Butler, and Connor Manning of Hobu, Inc.. Entwine and Entwine Point Tile were also designed and developed by Connor Manning of Hobu, Inc

Hobu, Inc.


COPC development was supported by

Microsoft Planetary Computer


There is no official pronunciation of COPC. Here are some possibilities:



Use Case

Cloud Optimized GeoTIFF has shown the utility and convenience of taking a dominant container format for geospatial raster data and optionally augmenting its organization to allow incremental “range-read” support over HTTP with it. With the mantra of “It’s just a TIFF” allowing ubiquitous usage of the data content combined with the flexibility of supporting partial reads over the internet, COG has found a sweet spot. Its reward is the ongoing rapid conversion of significant raster data holdings to COG-organized content to enable convenient cloud consumption of the data throughout the GIS industry.

What is the COG for point clouds? It would need to be similar in fit and scope to COG:

“Just a LAZ”

LAZ (LASZip) is the ubiquitous geospatial point cloud format. It is an augmentation of ASPRS LAS that utilizes an arithmetic encoder to efficiently compress the point content. It has seen a number of revisions, but the latest supports dimension-selective access and provides all of the metadata support that normal LAS provides. Importantly, multiple software implementations (laz-rs, laz-perf, and LASzip) provide LAZ compression and decompression, and laz-perf and laz-rs include compilation to JavaScript which is used by all JavaScript clients when consuming LAZ content.

Put EPT in LAZ

The EPT content organization supports LAZ in its current “exploded” organization. Exploded in this context means that each chunk of data at each octree level is stored as an individual LAZ file (or simple blob, or a zstd-compressed blob). One consequence of the exploded organization is large EPT trees of data can mean collections of millions of files. In non-cloud situations, EPT’s cost when moving data or deleting it can be significant. Like the tilesets of late 2000s raster map tiles, lots of little files are a problem.

LAZ provides a feature that allows concatenation of the individual LAZ files into a single LAZ file. This is the concept of a dynamically-sized chunk table. It is a feature that Martin Isenburg envisioned for quad-tree organized data, but it could work the same for an octree.

Structural Changes to Draft Specification