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Label Extension Specification (label)

Extension Maturity Classification: Proposal

This extension is meant to support using labeled AOIs with Machine Learning models, particularly training data sets, but can be used in any application where labeled AOIs are needed.

This document explains the fields of the STAC Label Extension to a STAC Item. It is used to describe labeled Areas of Interest (AOIs) that are used with earth observation imagery. These labels can take several forms, though all are expected to be contained with a GeoJSON FeatureCollection:

  • Tile classification labels: A GeoJSON FeatureCollection with a single Feature. This feature's geometry should match the bounds of the labeled image tile, and a Feature property should define the class (see below).
  • Tile regression labels: A GeoJSON FeatureCollection with a single Feature. This feature's geometry should match the bounds of the labeled image tile, and a Feature property should define the regression value (see below).
  • Object detection labels: A GeoJSON FeatureCollection containing rectangular bounding boxes (as Polygon geometry Features) defining the bounds of an object of interest (e.g. a car). A Feature property must define the class of the object labeled. Additional Feature properties may be defined for additional metadata.
  • Segmentation labels: A GeoJSON FeatureCollection containing Polygon geometry Features that trace the boundaries of objects of interest (e.g. buildings, vegetation, bodies of water), or raster-formatted pixel masks defining pixel classes. (See raster label notes)





Item fields

A Label Item represents a polygon, set of polygons, or raster data defining labels and label metadata and should be part of a Collection. See the raster label notes section below for details on raster-formatted labels. It is up to the data provider how to group their catalog, but a typical use might have a Collection of a series of label sets (Items) that are related. For example a "Building" collection might have 50 Items, each one was a set of building AOIs for a single country. The Collection holds details on the data providers and the license.

Like other content extensions, the Label extension adds additional fields to a STAC Item, which are detailed after some additional clarification on what the core fields mean with respect to a Label Item.

Core Item fields

Some additional notes are given here for some of the core STAC Item fields and what they represent for label.

  • bbox and geometry: The bounding box and the geometry of a Label Item represents the region for which the label(s) is/are valid. This could be the extent of all the AOIs in the dataset, or could be the region the provider believes the label is representative.
  • properties.datetime: The datetime of a Label Item is the nominal datetime for which the label applies, typically this is the datetime of the source imagery used to generate the labels. If the label applies over a range of datetimes (e.g., generated from multiple source images) then use the datetime-range (dtr) extension to indicate start and end datetimes.
  • assets: The label assets are GeoJSON FeatureCollection assets containing the actual label features. As with the core STAC Item a thumbnail asset is also strongly encouraged.

New Item properties

element type info name description
label:properties [string|null] Name REQUIRED These are the names of the property field(s) in each Feature of the label asset's FeatureCollection that contains the classes (keywords from label:classes if the property defines classes). If labels are rasters, use null.
label:classes [Class Object] Classes REQUIRED if using categorical data. A Class Object defining the list of possible class names for each label:properties. (e.g., tree, building, car, hippo)
label:description string Description REQUIRED A description of the label, how it was created, and what it is recommended for
label:type string Type REQUIRED An ENUM of either vector label type or raster label type
label:tasks [string] Task Recommended to be a subset of 'regression', 'classification', 'detection', or 'segmentation', but may be an arbitrary value
label:methods [string] Method Recommended to be a subset of 'automated' or 'manual', but may be an arbitrary value.
label:overviews [Label Overview Object] Overview An Object storing counts (for classification-type data) or summary statistics (for continuous numerical/regression data).

Class Object

Field Name Type name description
name string|null Name The property key within the asset's each Feature corresponding to class labels. If labels are raster-formatted, use null.
classes [string|number] Classes The different possible class values within the property name.

Label Overview Object

Field Name Type name description
property_key string Property Key The property key within the asset corresponding to class labels.
counts [Count Object] Counts An object containing counts for categorical data.
statistics [Stats Object] Statistics An object containing statistics for regression/continuous numeric value data.

label:overviews generally won't have both counts and statistics, but one of the two.

Count Object

Field Name Type name description
name string Class Name The different possible classes within the property name.
count integer Count The number of occurrences of the class.
    "property_key": "road_type",
    "counts": [
        "name": "dirt",
        "count": 10
        "name": "paved",
        "count": 99

Stats Object

Field Name Type name description
name string Stat Name The name of the statistic being reported.
value number Value The value of the statistic name.
    "property_key": "elevation",
    "statistics": [
        "name": "mean",
        "value": 100.1
        "name": "median",
        "value": 102.3
        "name": "max",
        "value": 100000


labels (required)

The Label Extension requires at least one asset that uses the key "labels". The asset will contain a link to the actual label data. The asset has these requirements:

  • is a GeoJSON FeatureCollection
  • if label:tasks is tile_classification, object_detection, or segmentation, each feature should have one or more properties containing the label(s) for the class (one of label:classes). the name of the property can be anything (use "label" if making from scratch), but needs to be specified in the Item with the label:properties field.
  • if label:tasks is tile_regression, each feature should have one or more properties defining the value for regression. the name of the property can be anything (use "label" if making from scratch), but needs to be specified in the Item with the label:properties field.
Raster Label Notes

If the labels are formatted as rasters - for example, a pixel mask with 1s where there is water and 0s where there is land - the following approach is recommended for including those data.

The raster label file (e.g. a GeoTIFF) should be included as an asset under the item. Along with the image file, a GeoJSON FeatureCollection asset should be included. That FeatureCollection should contain a single Feature, ideally a polygon geometry defining the extent of the raster.

Rendered images (optional)

The source imagery used for creating the label is linked to under links (see below). However the source imagery is likely to have been rendered in some way when creating the training data. For instance, a byte-scaled true color image may have been created from the source imagery. It may be useful to save this image and include it as an asset in the Item.

Links: source imagery

A Label Item links to any source imagery that the AOI applys to by linking to the STAC Item representing the imagery. Source imagery is indicated by using a rel type of "source" and providing the link to the STAC Item.

In addition the source imagery link has a new label extension specific field:

element type info name description
label:assets [string] Assets The keys for the assets within the source item to which this label item applies.

The label:assets field applies to situations where the labels may apply to certain assets inside the source imagery Item, but not others (e.g. if the labels were traced on top of RGB imagery, but the source item also contains assets for a Digital Elevation Model).


Example implementations can be found in Examples. The Roads implementation provides an example item for labels from the SpaceNet Road Network Extraction Challenge Dataset, providing segmentation labels for road networks. The Misc Samples implementation provides an example catalog of collections with sample label items from several training datasets, SpaceNet Buildings and Open AI Tanzania Building Footprint Segmentation Challenge for now, providing segmentation labels for buildings.

Raster Foundry will support exporting STAC-compliant training data label items, assets, and sources all in json format contained in a zip file for a project layer. There is a Pull Request for the backend support in its repository. Frontend support is on the roadmap.


Label Items may often use the datetime-range extension if the label set applies over a range of dates. While the EO extension doesn't make sense within a Label Item itself, most Label Items will link to source data which will frequently use the EO Extension. The extensions page gives an overview about these and other extensions.

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