Radiant MLHub

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/radiant-mlhub-dataset")

fs.listdir("s3://radiant-mlhub")

Description:

Radiant MLHub is an open library for geospatial training data that hosts datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation.

Contact:

Radiant MLHub is an open library for geospatial training data that hosts datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation.

Update Frequency:

New training data catalogs are added on a rolling basis

Managed By:

https://www.radiant.earth/

Resources:

1. resource:
   • Description: Radiant MLHub Training Data
   • ARN: arn:aws:s3:::radiant-mlhub
   • Region: us-west-2
   • Type: S3 Bucket

Tags:

aws-pds, labeled, machine learning, geospatial, earth observation, satellite imagery, sustainability, environmental, cog, stac

Tutorials:

1. tutorial:

   • Title: How to access Radiant MLHub Data
   • URL: https://github.com/radiantearth/mlhub-tutorials/blob/master/RadiantMLHub-intro.pdf
   • AuthorName: Radiant Earth
   • AuthorURL: https://www.radiant.earth/

2. tutorial:

   • Title: Radiant MLHub Tutorials with Jupyter Notebooks
   • URL: https://github.com/radiantearth/mlhub-tutorials/tree/master/notebooks
   • AuthorName: Kevin Booth
   • AuthorURL: https://www.linkedin.com/in/kbgg/

3. tutorial:

   • Title: Explore wind speed images from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL)
   • URL: https://github.com/aws-samples/asdi-smsl-wind-speed-data/
   • NotebookURL: https://github.com/aws-samples/asdi-smsl-wind-speed-data/blob/main/wind-speed-data.ipynb
   • AuthorName: Frank Rubino
   • Services: SageMaker Studio Lab

Tools & Applications:

1. tools & applications:

   • Title: Radiant MLHub Dataset Registry
   • URL: https://registry.mlhub.earth/
   • AuthorName: Radiant Earth
   • AuthorURL: https://www.radiant.earth/

2. tools & applications:

   • Title: Challenge on Computer Vision for Crop Detection from Satellite Imagery
   • URL: https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition
   • AuthorName: Radiant Earth
   • AuthorURL: https://www.radiant.earth/

Publication:

1. publication:

   • Title: A Guide for Collecting and Sharing Ground Reference Data for Machine Learning Applications
   • URL: https://medium.com/radiant-earth-insights/a-guide-for-collecting-and-sharing-ground-reference-data-for-machine-learning-applications-90664930925e
   • AuthorName: Yonah Bromberg Gaber
   • AuthorURL: https://www.linkedin.com/in/yonahbg/

2. publication:

   • Title: Geo-Diverse Open Training Data as a Global Public Good
   • URL: https://aws.amazon.com/blogs/publicsector/geo-diverse-open-training-data-as-a-global-public-good/
   • AuthorName: Hamed Alemohammad
   • AuthorURL: https://www.linkedin.com/in/hamedalemohammad/

3. publication:

   • Title: Creating a Machine Learning Commons for Global Development
   • URL: https://medium.com/radiant-earth-insights/creating-a-machine-learning-commons-for-global-development-256ef3dd46aa
   • AuthorName: Hamed Alemohammad
   • AuthorURL: https://www.linkedin.com/in/hamedalemohammad/