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Showing content from https://github.com/stac-extensions/classification/ below:

stac-extensions/classification: Describes categorical values and bitfields to give values in a file a certain meaning (classification).

Classification Extension Specification

Title: Classification
Identifier: https://stac-extensions.github.io/classification/v2.0.0/schema.json
Field Name Prefix: classification
Scope: Item, Collection
Extension Maturity Classification: Pilot
Owner: @drwelby @m-mohr @pjhartzell

This document explains the Classification Extension to the SpatioTemporal Asset Catalog (STAC) specification.

Examples:
- Asset level:
  - Raster Classes example: STAC Item with classified raster bands in asset (Maxar)
  - Bitfields example: STAC Item with bit fields in asset (Landsat)
- Item level:
  - Model Classes example: STAC Item with classes of a Machine Learning Model output
- Collection level:
  - Item Assets example: STAC Collection using Item Assets for classed child Items
JSON Schema
Changelog

Note that Classification in this context is about providing semantic information on the pixel content. It does not relate in any way to security classification or other confidentiality labeling.

Field Name Type Description classification:classes [Class] Classes stored in raster or bands classification:bitfields [Bit Field] Classes stored in bit fields in the raster

classification:classes is for when one or more unique coded integer values are present within a raster asset or band therein. These coded values translate to classes of data with verbose descriptions.

An example would be a cloud mask raster that stores values that represent image conditions in each pixel.

classification:bitfields is for classes that are stored in fields of continuous bits within the pixel's value. Files using this strategy are commonly given the name 'bit mask' or 'bit index'. The values stored are the integer representation of the bits in the field when summed as an isolated string. Bits are always read right to left. The position of the first bit in a field is given by its offset. Therefore the first (rightmost) bit is at offset zero.

These classification objects can be used in the following places:

In a raster Asset object if single band.
For multiband rasters, use raster:bands and store the classes in each Band Object.
In a Machine Learning Model output object to define the classes represented within that output.
As an item-assets field in a Collection object, to indicate that the classification is used across child Items.

Describes multiple classes stored in a field of a continuous range of bits

Field Name Type Description offset integer REQUIRED. Offset to first bit in the field length integer REQUIRED. Number of bits in the field classes [Class] REQUIRED. Classes represented by the field values name string Short name of the class for machine readability. Must consist only of letters, numbers, -, and _ characters. description string A short description of the classification. CommonMark 0.29 syntax MAY be used for rich text representation. roles [string] see Asset Roles

A Bit Field stores classes within a range of bits in a data value. The range is described by the offset of the first bit from the rightmost position, and the length of bits used to store the class values.

Since bit fields are often used to store data masks, they can also use optional STAC roles to identify their purpose to clients.

Following is a simplified example a bit field scheme for cloud data using 4 bits. The bits are broken into 3 bit fields.

3210
||||
...X   - 1 here means "no data", 0 means "valid data"
..X.   - 1 here means "cloud pixel", 0 means "clear pixel"
XX..   - these two bits are "cloud confidence" and give 4 classes (binary 00, 01, 10, and 11)

Working right to left, these four bits represent 3 bit fields:

...X is offset:0, length:1
..X. is offset:1, length:1
XX.. is offset:2, length:2

To extract the values in a bit field from a value called data, you typically would use the expression:

data >> offset & (1<<length) - 1

This does:

data >> offset right-shifts the bits in the data value offset number of times
(1 << length) - 1 gives us length number of 1 bits going right to left, which gives us a bit mask
ANDing (&) the values together gives the binary representation of the class value

An example of finding the cloud confidence class value from the 4 bit example above for the data value of integer 6:

Integer 6 is 0110 in binary
We want to extract the field at offset:2, length:2
First, right-shift twice (0110 >> 2), which results in 1001
Next, make the bit mask, which is 2 left shifts of 0001 to get 0100 (integer 4), then subtract 1 to get 0011 (integer 3)
AND these two values together 1001 & 0011 and you get 0001, or integer 1
Therefore at this pixel the cloud confidence field is storing class 1

The key distinction with bit fields from other types of bit masks is that the bits in the field are summed as standalone bits. Therefore 01.. cloud confidence class uses the value of 1, not 4 (binary 0100)

For a real world example, see Landsat 8's Quality raster.

Describes a data class

Field Name Type Description value integer REQUIRED. Value of the class name string REQUIRED. Short name of the class for machine readability. Must consist only of letters, numbers, -, and _ characters. title string Human-readable name for use in, e.g., a map legend. description string Description of the class. CommonMark 0.29 syntax MAY be used for rich text representation. color_hint string Suggested color for rendering (Hex RGB code in upper-case without leading #) nodata boolean If set to true classifies a value as a no-data value, defaults to false percentage number The percentage of data values that belong to this class in comparison to all data values, in percent (0-100). count integer The number of data values that belong to this class.

Class objects enumerate data values and their corresponding classes. A cloud mask raster could contain the following four classes:

0: "No data"
1: "Clear"
2: "Cloud"
3: "Cloud shadow"

color_hint only is intended to hint a reasonable color for clients to use and is not intended to define styling. For example, the ESA landcover datasets use "color_hint":"006400" to suggest using a green color for a class of "Tree cover".

For conveying styling see the Raster Extension and cpt2json for discussion on passing styling as an item asset instead.

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.

The same checks that run as checks on PRs 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:

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

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:

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