Getting Started

This guide will walk you through the basic usage of the radiant_mlhub library, including:

  • Installing & configuring the library

  • Discovering & fetching datasets

  • Discovering & fetching collections

  • Downloading dataset STAC catalog and assets

Background Info

If you have not already, browse Radiant MLHub to discover the datasets and ML models which are currently published on MLHub. Consider browsing the STAC specification to learn about SpatioTemporal Asset Catalogs (STAC). The MLHub API serves STAC Collections, Items and Assets.


Install with pip

$ pip install radiant_mlhub

Install with conda

$ conda install -c conda-forge radiant-mlhub


If you have not done so already, you will need to register for an MLHub API key at

Once you have your API key, you will need to create a default profile by setting up a .mlhub/profiles file in your home directory. You can use the mlhub configure command line tool to do this:

$ mlhub configure
API Key: Enter your API key here...
Wrote profile to /home/user/.mlhub/profiles


If you do not have write access to the home directory on your machine, you can change the location of the profiles file using the MLHUB_HOME environment variables. For instance, setting MLHUB_HOME=/tmp/some-directory/.mlhub will cause the client to look for your profiles in a /tmp/some-directory/.mlhub/profiles file. You may want to permanently set this environment variable to ensure the client continues to look in the correct place for your profiles.

List Datasets

Once you have your profile configured, you can get a list of the available datasets from the Radiant MLHub API using the Dataset.list method. Remember that all datasets are also browseable and searchable on Radiant MLHub.

>>> from radiant_mlhub import Dataset
>>> datasets = Dataset.list()
>>> # print the first 5 datasets for example
>>> for dataset in datasets[0:5]:
...     print(dataset)
umd_mali_crop_type: 2019 Mali CropType Training Data
idiv_asia_crop_type: A crop type dataset for consistent land cover classification in Central Asia
dlr_fusion_competition_germany: A Fusion Dataset for Crop Type Classification in Germany
ref_fusion_competition_south_africa: A Fusion Dataset for Crop Type Classification in Western Cape, South Africa
bigearthnet_v1: BigEarthNet

Fetch a Dataset

You can also fetch a dataset by ID using the Dataset.fetch method. This method returns a Dataset instance.

>>> dataset = Dataset.fetch('bigearthnet_v1')
>>> print(dataset)
bigearthnet_v1: BigEarthNet V1

Work with Dataset Collections

Datasets have one or more collections associated with them. Collections fall into two types:

  • source_imagery: Collections of source imagery associated with the dataset

  • labels: Collections of labeled data associated with the dataset (these collections implement the STAC Label Extension)

To list all the collections associated with a dataset use the collections attribute.

>>> dataset.collections
[<Collection id=bigearthnet_v1_source>, <Collection id=bigearthnet_v1_labels>]
>>> type(dataset.collections[0])

You can also list the collections by type using the collections.source_imagery and collections.labels properties. This example code shows that collections are actually STAC objects.

>>> from pprint import pprint
>>> len(dataset.collections.source_imagery)
>>> source_collection = dataset.collections.source_imagery[0]
>>> pprint(source_collection.to_dict())
{'description': 'BigEarthNet v1.0',
'extent': {'spatial': {'bbox': [[-9.00023345437725,
            'temporal': {'interval': [['2017-06-13T10:10:31Z',
'id': 'bigearthnet_v1_source',
'license': 'CDLA-Permissive-1.0',
'links': [{'href': '',
            'rel': 'items',
            'type': 'application/geo+json'},
        {'href': '',
            'rel': 'parent',
            'type': 'application/json'},
        {'href': '',
            'rel': <RelType.ROOT: 'root'>,
            'title': 'Radiant MLHub API',
            'type': <MediaType.JSON: 'application/json'>},
        {'href': '',
            'rel': 'self',
            'type': 'application/json'}],
'providers': [{'name': 'BigEarthNet',
                'roles': ['processor', 'licensor'],
                'url': ''}],
'sci:citation': 'G. Sumbul, M. Charfuelan, B. Demir, V. Markl, "BigEarthNet: '
                'A Large-Scale Benchmark Archive for Remote Sensing Image '
                'Understanding", IEEE International Geoscience and Remote '
                'Sensing Symposium, pp. 5901-5904, Yokohama, Japan, 2019.',
'sci:doi': '10.14279/depositonce-10149',
'stac_extensions': [''],
'stac_version': '1.0.0',
'type': 'Collection'}

Download a Dataset

You can download a dataset’s STAC catalog, and all of it’s linked assets, using the method. Consider checking the dataset size before downloading. Here is an example dataset which is relatively small in size. The downloader can also scale up to the largest datasets.

>>> dataset = Dataset.fetch('nasa_marine_debris')
>>> print(dataset)
nasa_marine_debris: Marine Debris Dataset for Object Detection in Planetscope Imagery
>>> print(dataset.stac_catalog_size)  # OK the STAC catalog archive is only ~260KB
>>> print(dataset.estimated_dataset_size)  # OK the total dataset assets are ~77MB
nasa_marine_debris: fetch stac catalog: 258KB [00:00, 412.53KB/s]
INFO:radiant_mlhub.client.catalog_downloader:unarchive nasa_marine_debris.tar.gz ...
unarchive nasa_marine_debris.tar.gz: 100%|████████████████████| 2830/2830 [00:00<00:00, 5772.09it/s]
INFO:radiant_mlhub.client.catalog_downloader:create stac asset list (please wait) ...
INFO:radiant_mlhub.client.catalog_downloader:2825 unique assets in stac catalog.
download assets: 100%|██████████████████████| 2825/2825 [03:27<00:00, 13.62it/s]
INFO:radiant_mlhub.client.catalog_downloader:assets saved to nasa_marine_debris

The method saves the STAC catalog and assets into your current working directory (by default).

The downloader has the ability to download in parallel with many cores, resume interrupted downloads, as well as options for filtering the assets to a more manageable size (highly recommended, depending on your application).


The Datasets guide has more downloading examples and the API reference is available as well.


The Collections guide has examples of downloading collection archives. Collection archives are not available for all collections, so consider using the Dataset downloader instead.

Discovering ML Models

ML Models are discoverable through the Python client as well. See the ML Models guide for more information.