ML Models

A Model represents a STAC Item implementing the ML Model extension. The goal of the ML Model Extension is to provide a way of cataloging machine learning models that operate on earth observation (EO) data described as a STAC catalog.

To discover and fetch models you can either use the MLModel class or the low-level client methods from radiant_mlhub.client. Using the MLModel class is the recommended approach, but both methods are described below.


The Radiant MLHub web application provides an overview of all the ML models available through the Radiant MLHub API.

Discovering Models

You can discover models using the MLModel.list method. This method returns a list of MLModel instances.

>>> from radiant_mlhub import MLModel
>>> models = MLModel.list()
>>> first_model = models[0]
>>> for model in models[0:2]:  # print first two models, for example
>>>     print(model)
model-crop-detection-torchgeo-v1: A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery
model-cyclone-wind-estimation-torchgeo-v1: Tropical Cyclone Wind Estimation Model

You can fetch a model by ID using MLModel.fetch method.

>>> model = MLModel.fetch('model-cyclone-wind-estimation-torchgeo-v1')
>>> model.assets
.. {'inferencing-compose': <Asset href=>,
.. 'inferencing-checkpoint': <Asset href=>}
>>> len(first_model.links)
>>> # print only the ml-model and mlhub related links
>>> from pprint import pprint
>>> pprint([ link for link in first_model.links if 'ml-model:' in link.rel or 'mlhub:' in link.rel])
.. [<Link rel=ml-model:inferencing-image target=docker://>,
.. <Link rel=ml-model:train-data target=>,
.. <Link rel=ml-model:train-data target=>,
.. <Link rel=mlhub:training-dataset target=>]
>>> # you can access rest of properties as a dict
.. dict_keys(['title', 'license', 'sci:doi', 'datetime', 'providers', 'description', 'end_datetime', 'sci:citation', 'ml-model:type', 'start_datetime', 'sci:publications', 'ml-model:training-os', 'ml-model:architecture', 'ml-model:prediction_type', 'ml-model:learning_approach', 'ml-model:training-processor-type'])

Low-level Client

The Radiant MLHub /models endpoint returns a list of objects describing the available models and their properties. You can use the low-level list_models() function to work with these responses as native Python data types (list and dict).

>>> from radiant_mlhub.client import list_models
>>> models = list_models()
>>> first_model = models[0]
>>> first_model.keys()
dict_keys(['id', 'bbox', 'type', 'links', 'assets', 'geometry', 'collection', 'properties', 'stac_version', 'stac_extensions'])
>>> first_model['id']
>>> first_model['properties'].keys()
dict_keys(['title', 'license', 'sci:doi', 'datetime', 'providers', 'description', 'end_datetime', 'sci:citation', 'ml-model:type', 'start_datetime', 'sci:publications', 'ml-model:training-os', 'ml-model:architecture', 'ml-model:prediction_type', 'ml-model:learning_approach', 'ml-model:training-processor-type'])

Fetching Model Metadata

The Radiant MLHub /models/{model_id} endpoint returns an object representing a single model. You can use the low-level get_model_by_id() function to work with this response as a dict.

>>> from radiant_mlhub.client import get_model_by_id
>>> model = get_model_by_id('model-cyclone-wind-estimation-torchgeo-v1')
>>> model.keys()
dict_keys(['id', 'bbox', 'type', 'links', 'assets', 'geometry', 'collection', 'properties', 'stac_version', 'stac_extensions'])

You can also fetch a model from the Radiant MLHub API based on the model ID using the MLModel.fetch method. This is the recommended way of fetching a model. This method returns a MLModel instance.

>>> from radiant_mlhub import MLModel
>>> model = MLModel.fetch('model-cyclone-wind-estimation-torchgeo-v1')
>>> len(model.assets)
>>> len(model.links)