4.1.1. array

4.1.2. chunk

4.1.3. coordinate variable

4.1.4. data variable

4.1.5. dimension

4.1.6. group

4.1.7. metadata

4.1.8. multiscale dataset

4.1.9. tile matrix set

4.1.10. transform

4.1.11. unified data model (UDM)

7.2.1.  Common Data Model (CDM)

The CDM defines a generalised schema for representing array-based scientific datasets. The following constructs are reused directly within the unified model:

The unified data model adopts these CDM components without modification excluding the user-defined types. Semantic interpretation remains consistent with the original CDM specification. GeoZarr structures are mapped to CDM constructs to ensure compatibility and clarity.

7.2.2.  CF Conventions

The CF Conventions specify standardised metadata attributes and practices to describe spatiotemporal context within CDM-compliant datasets. These conventions support consistent interpretation of:

The unified data model supports CF-compliant metadata, including attributes such as standard_name, units, and grid_mapping. The unified data model does not prescribe CF compliance but enables it through permissive design. Partial adoption of CF attributes is supported, and non-compliant datasets may selectively adopt CF metadata as needed.

7.2.3.  Standards-Based Extensions

To support additional capabilities, the model defines optional extension points referencing external OGC and community standards:

These extensions are integrated in a modular fashion and do not alter the core semantics of the CDM or CF structures. Implementations may selectively adopt these extensions based on their application requirements.

7.3.1.  Multi-Resolution Overviews (OGC Tile Matrix Set)

Support for multi-resolution imagery is enabled via integration with the OGC Tile Matrix Set Standard:

This approach aligns with the OGC API – Tiles and enables efficient access to large gridded datasets.

7.3.2.  GeoTransform Metadata (GDAL Interpolation and Affine Transform)

Geospatial referencing can be further refined through the inclusion of metadata consistent with GDAL conventions:

This extension augments CF grid mappings by providing precise control over grid placement and coordinate transformations.

7.3.3.  STAC Collection and Item Integration

To enable discovery of resources within the hierarchical structure of the data model, this Standard supports the inclusion of STAC metadata elements at appropriate locations within the group hierarchy.

A STAC extension consists of embedding or referencing STAC Collection and Item metadata within the data model:

STAC integration is non-intrusive and modular. It does not impose changes on the internal organisation of datasets and MAY be adopted incrementally by implementations requiring catalogue-based discovery capabilities.

7.3.4.  Modularity and Interoperability

Each extension point is specified independently. Implementations may advertise support for one or more extensions by declaring conformance to corresponding extension modules. This modularity facilitates incremental adoption, promotes reuse, and enhances interoperability across varied implementation environments.

7.4.1.  Dataset Structure

A dataset conforming to the Unified Data Model (UDM) is structured as a hierarchy rooted at a top-level dataset entity. This design enables modularity and facilitates the representation of complex, multi-resolution, or thematically partitioned data collections.

Each dataset node comprises the following core components, aligned with the Unidata Common Data Model (CDM) and Climate and Forecast (CF) Conventions:

The hierarchy is implemented through groups, which function as containers for variables, dimensions, and metadata. Groups may define local context while inheriting attributes from parent nodes. This supports the logical subdivision of datasets by theme, resolution, or processing stage, and enhances the clarity and reusability of complex geospatial structures.

The diagram below represents the structural layer of the unified data model, derived from the Unidata Common Data Model, which serves as the foundational framework for supporting all overlaying model layer.

Figure 1 — Conformance-class model

Note that, conceptually, node within this hierarchy might be treated as a self-contained dataset.

7.4.2.  Coordinate Referencing

Coordinate systems are defined using:

The model accommodates both standard CF-compatible definitions and extended referencing mechanisms to support use cases that span scientific analysis and geospatial mapping.

7.4.3.  Metadata Integration

Metadata may be declared at various levels within the model structure:

All metadata follows harmonised naming and semantics consistent with the CDM and CF standards, enabling machine and human interpretability while supporting metadata exchange across diverse systems.

7.5.1.  Introduction

Overviews are downscaled representations of gridded data designed to optimise visualisation and scalable access to large datasets. Overviews or multiscale pyramid provide lower-resolution versions of the same variables, enabling rapid display, efficient zooming, and progressive data exploration. Multiple overview levels may exist, each representing the same data at a coarser spatial resolution.

The Overviews construct extends the Common Data Model (CDM) by defining a hierarchical organisation of groups and variables that represent data at multiple scales. The OverviewSet is self-described by attributes defined at the parent group level, which declare the relationships between its levels and ensure consistent, interoperable multiscale representation within the CDM framework.

7.5.2.  Purpose and Scope

The Overviews extension enables scalable access to multidimensional gridded data, particularly for geospatial and remote sensing applications. It supports:

This specification is format-agnostic and may be implemented in any CDM-compliant structure, regardless of physical encoding (e.g. Zarr, NetCDF, GeoTIFF) although the present specification specifically targets Zarr.

7.5.3.  Conceptual Model

The Overviews construct defines a multiscale hierarchy applied to the variable group, i.e., the CDM group containing the data variables and their associated metadata, and optionally other related variables that share identical dimensions and coordinate systems.

Example: Typical CDM Group Structure without overviews

Group: variable_group/
├── variable1
├── variable2
├── aux_variable
├── coordinates1
├── coordinates2
└── Attributes

Listing 1

Each overview level provides a reduced-resolution representation of the same variables. This approach avoids redundancy by describing the hierarchy once for the entire group rather than for individual variables, ensuring consistency and concise metadata.

All overview levels are semantically equivalent, differing only in resolution, array extent, or sampling density. There is no requirement for a single base or reference level—each level may serve as an entry point depending on the application.

7.5.4.  Model Components

The Overviews construct defines the conceptual elements used to represent multiscale data within the GeoZarr data model. It extends the existing CDM concept to support datasets provided at multiple spatial resolutions.

The construct introduces the following conceptual elements:

Table 2

The OverviewSet retains the same structure as a nominal variable group, including the associated metadata and auxiliary variables, so that the multiscale hierarchy preserves the complete descriptive context of the original dataset

Example

7.5.5.  Structural Layout

The Overviews construct is expressed within the Common Data Model (CDM) framework, which represents datasets through groups, variables, and attributes.

An OverviewSet corresponds to a CDM group containing multiple OverviewLevels, each representing the same data variables at different spatial resolutions.

Within this structure:

The complete description of the hierarchy is provided by the multiscales property, an attribute of the OverviewSet group that lists the available overview levels, their identifiers, and any associated information such as resampling methods or grid references.

Group: reflectance/                     # OverviewSet (Group)
├── Attribute: multiscales              # Metadata describing the multiscale hierarchy
├── Attribute: spatial_ref = "EPSG:32633"
├── Auxiliary Variable: quality_flag
├── Group: L0/                          # OverviewLevel (highest or nominal resolution)
│    ├── Variable: b01
│    ├── Variable: b02
│    ├── Variable: b03
│    ├── Coordinate Variable: x
│    └── Coordinate Variable: y
├── Group: L1/                          # OverviewLevel (coarser resolution)
│    ├── Variable: b01
│    ├── Variable: b02
│    ├── Variable: b03
│    ├── Coordinate Variable: x
│    └── Coordinate Variable: y
└── Group: L2/                          # OverviewLevel (coarsest resolution)
     ├── Variable: b01
     ├── Variable: b02
     ├── Variable: b03
     ├── Coordinate Variable: x
     └── Coordinate Variable: y

7.5.6.  OverviewSet Metadata

The multiscales property is an attribute of the OverviewSet group that formally defines the organisation of the multiscale hierarchy. It provides a structured description of all overview levels, their ordering, and the resampling or aggregation relationships between them.

The property SHALL be encoded as a structured object formally defined as a JSON Schema available at: Multiscales JSON Schema

It defines global attributes applying to the entire hierarchy and a layout array that lists all overview levels in order of resolution.

{
  "version": "1.0",
  "resampling_method": "average",
  "tile_matrix_ref": "OGC:WMT:1.0:WebMercatorQuad",
  "layout": [
    {
      "id": "L0",
      "path": "L0",
      "cell_size": [10.0, 10.0]
    },
    {
      "id": "L1",
      "path": "L1",
      "derived_from": "L0",
      "factors": [2, 2],
      "cell_size": [20.0, 20.0],
      "resampling_method": "average"
    },
    {
      "id": "L2",
      "path": "L2",
      "derived_from": "L1",
      "factors": [2, 2],
      "cell_size": [40.0, 40.0],
      "resampling_method": "average"
    }
  ]
}

7.6.1.  Core Conformance

7.6.2.  Extension Conformance

7.6.3.  Conformance Classes

7.6.4.  Extensibility Principles

This extensibility framework supports both minimum-viable use and high-fidelity metadata integration, enabling incremental adoption across the geospatial and scientific data communities.

7.7.1.  Format Mapping and Alignment

The data model is explicitly aligned with foundational standards including the Unidata Common Data Model (CDM), the CF Conventions, and established practices in formats such as NetCDF and GeoTIFF. Where applicable, GeoZarr datasets may be derived from or transformed into these formats using consistent mappings.

These mappings facilitate round-trip transformations and enable toolchains that consume or produce multiple formats without reengineering semantic models.

7.7.2.  Semantic Interoperability

Semantic interoperability is supported through adherence to CF conventions, use of standardised attribute names (e.g., standard_name, units), and alignment with metadata vocabularies used in other ecosystems (e.g., STAC, EPSG codes, ISO 19115 keywords).

The model does not prescribe specific vocabularies beyond CF but encourages reuse and recognition of widely accepted descriptors to promote cross-domain understanding.

7.7.3.  Metadata and Discovery Integration

STAC compatibility enables integration with catalogue services for discovery and indexing. Datasets can expose STAC-compliant metadata alongside core metadata, supporting federated search and filtering via STAC APIs.

This approach enables seamless integration into modern data catalogues and platforms that support EO discovery standards.

7.7.4.  Tool and Ecosystem Support

The unified data model facilitates interoperability with tools and libraries across the following domains:

Tooling support is expected to grow via standard-conformant implementations, easing adoption across domains and infrastructures.

9.7.1.  Relationship to Core CDM Encoding

The encoding of overviews reuses the same mapping rules established in the core data model encoding:

9.7.2.  Example Encoding (Zarr v3)

{
  "zarr_format": 3,
  "node_type": "group",
  "attributes": {
    "multiscales": {
      "version": "1.0",
      "resampling_method": "average",
      "tile_matrix_ref": "OGC:WMT:1.0:WebMercatorQuad",
      "layout": [
        {
          "id": "L0",
          "path": "L0",
          "cell_size": [10.0, 10.0]
        },
        {
          "id": "L1",
          "path": "L1",
          "derived_from": "L0",
          "factors": [2, 2],
          "cell_size": [20.0, 20.0],
          "resampling_method": "average"
        },
        {
          "id": "L2",
          "path": "L2",
          "derived_from": "L1",
          "factors": [2, 2],
          "cell_size": [40.0, 40.0],
          "resampling_method": "average"
        }
      ]
    },
    "spatial_ref": "EPSG:32633"
  },
  "metadata": {
    "title": "Reflectance Multiscale Example",
    "summary": "Example of a multiscale dataset encoded using the GeoZarr Overviews extension."
  }
}

Listing 8

Child groups represent the overview levels:

reflectance/
├── zarr.json                      # OverviewSet metadata (includes "multiscales")
├── L0/
│   ├── zarr.json                  # Highest or nominal resolution
│   ├── b01/
│   ├── b02/
│   ├── x/
│   └── y/
├── L1/
│   ├── zarr.json
│   ├── b01/
│   ├── b02/
│   ├── x/
│   └── y/
└── L2/
├── zarr.json
├── b01/
├── b02/
├── x/
└── y/

Listing 9