Table Extension Specification

• Title: Table
• Identifier: https://stac-extensions.github.io/table/v1.2.0/schema.json
• Field Name Prefix: table
• Scope: Item, Collection
• Extension Maturity Classification: Pilot
• Owner: @TomAugspurger

This document explains the table Extension to the SpatioTemporal Asset Catalog (STAC) specification. It can be used with the projection extension to describe geospatial tabular data.

An Collection or Item can describe a tabular data assets, using a list of Column objects. Additionally, Collections can describe many tabular datasets using Table objects.

• Examples:
  • Item example: Shows the basic usage of the extension in a STAC Item
  • Collection example: Shows the basic usage of the extension in a STAC Collection
• JSON Schema
• Changelog

Item Properties and Collection Fields

Field Name	Type	Description
table:columns	[ Column Object ]	A list of (#column objects) describing each column.
table:primary_geometry	string	The primary geometry column name.
table:row_count	number	The number of rows in the dataset.

table:primary_geometry Is the column name of the "primary" or "active" geometry. This is used by libraries like geopandas and sf to control which geometry column is used. When a STAC item uses both the projection and table extensions, it's understood that the values in proj:espg, proj:bbox, etc. refer to the primary_geometry column.

Column Object

Column objects contain information about each colum in the table.

Field Name	Type	Description
name	string	REQUIRED. The column name
description	string	Detailed multi-line description to explain the dimension. CommonMark 0.29 syntax MAY be used for rich text representation.
type	string	Data type of the column. If using a file format with a type system (like Parquet), we recommend you use those types.

Asset Object fields

The following fields can be used for assets (in the Asset Object).

Field Name	Type	Description
table:storage_options	Map<string, any>	Additional keywords for opening the dataset.

table:storage_options can be used with fsspec to specify additional keywords necessary to open the data. For example, an asset might use {"account_name": "ai4edataeuwest"} to indicate that the asset is in the ai4edataeuwest storage account. Libraries like adlfs use this information to open the dataset.

Collection Fields

The following fields apply only to Collections. They can be used to catalog a collection of tables, where each table is stored as an Item, without having to include column-level metadata from each table on the Collection.

Field Name	Type	Description
table:tables	Map<string, Table Object>	REQUIRED A mapping of table names to

Table Object

Table objects contain high-level summaries about a table.

Field Name	Type	Description
name	string	REQUIRED. The table name
description	string	Detailed multi-line description to explain the dimension. CommonMark 0.29 syntax MAY be used for rich text representation.

Best Practices

STAC allows for some flexibility in how to catalog assets. In general, we recommend using the following hierarchy:

• Use Collection objects to catalog a dataset consisting of one or more tables.
• Use Item objects to catalog an individual table.

For a dataset consisting of a single table or many tables with the same schema (for example gbif, which provides snapshots of the same database at different points in time), you might include table:columns on the Collection itself, or both the Collection and items.

For datasets with many tables (for example, USF Forest Inventory and Analysis), we recommend cataloging just the tables at the Collection level in table:tables, and cataloging the the columns at just the Item level in table:columns on each Item.

Contributing

All contributions are subject to the STAC Specification Code of Conduct. For contributions, please follow the STAC specification contributing guide Instructions for running tests are copied here for convenience.

Running tests

The same checks that run as checks on PR's are part of the repository and can be run locally to verify that changes are valid. To run tests locally, you'll need npm, which is a standard part of any node.js installation.

First you'll need to install everything with npm once. Just navigate to the root of this repository and on your command line run:

npm install

Then to check markdown formatting and test the examples against the JSON schema, you can run:

npm test

This will spit out the same texts that you see online, and you can then go and fix your markdown or examples.

If the tests reveal formatting problems with the examples, you can fix them with:

npm run format-examples