Data

Tools

Indexes

Applications

Experiments

Open Sustainable Technology

A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

Clay Foundation Model

Clay is a foundational model of Earth using a vision transformer architecture adapted to understand geospatial and temporal relations on Earth Observation data.
https://github.com/clay-foundation/model

Category: Sustainable Development
Sub Category: Data Catalogs and Interfaces

Keywords

digital-elevation-model earth-observation embeddings foundation-model sentinel-1 sentinel-2

Keywords from Contributors

pangeo gdal stac energy-system-model conda-packages conda data-catalog conda-environment glaciers transforms

Last synced: about 18 hours ago
JSON representation

Repository metadata

The Clay Foundation Model - An open source AI model and interface for Earth

README.md

Clay Foundation Model

Jupyter Book Badge
Deploy Book Status

An open source AI model and interface for Earth.

Quickstart

Launch into a JupyterLab environment on

Binder SageMaker Studio Lab
Binder Open in SageMaker Studio Lab

Installation

Basic

To help out with development, start by cloning this repo-url

git clone <repo-url>
cd model

Then we recommend using mamba
to install the dependencies. A virtual environment will also be created with Python and
JupyterLab installed.

mamba env create --file environment.yml

[!NOTE]
The command above has been tested on Linux devices with CUDA GPUs.

Activate the virtual environment first.

mamba activate claymodel

Finally, double-check that the libraries have been installed.

mamba list

Usage

Running jupyter lab

mamba activate claymodel
python -m ipykernel install --user --name claymodel  # to install virtual env properly
jupyter kernelspec list --json                       # see if kernel is installed
jupyter lab &

Running the model

The neural network model can be ran via
LightningCLI v2.
To check out the different options available, and look at the hyperparameter
configurations, run:

python trainer.py --help

To quickly test the model on one batch in the validation set:

python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml --trainer.fast_dev_run=True

To train the model:

python trainer.py fit --model ClayMAEModule --data ClayDataModule --config configs/config.yaml

More options can be found using python trainer.py fit --help, or at the
LightningCLI docs.

Contributing

Writing documentation

Our Documentation uses Jupyter Book.

Install it with:

pip install -U jupyter-book

Then build it with:

jupyter-book build docs/

You can preview the site locally with:

python -m http.server --directory _build/html

There is a GitHub Action on ./github/workflows/deploy-docs.yml that builds the site and pushes it to GitHub Pages.

Owner metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 4 days ago

Total Commits: 123
Total Committers: 19
Avg Commits per committer: 6.474
Development Distribution Score (DDS): 0.707

Commits in past year: 45
Committers in past year: 16
Avg Commits per committer in past year: 2.813
Development Distribution Score (DDS) in past year: 0.6

Name Email Commits
Daniel Wiesmann y****p 36
Wei Ji 2****4 36
Bruno Sánchez-Andrade Nuño b****n@g****m 14
Soumya Ranjan Mohanty v****2@g****m 11
Lilly Thomas l****y@d****g 8
pre-commit-ci[bot] 6****] 4
Maxime Lenormand 4****d 2
SRM s****a@d****g 1
Bill Morris b****l@b****o 1
Brayden Zhang 6****g 1
Chuck Daniels c****4@g****m 1
Dan Bonomo d****o@g****m 1
Ferdinand Schenck f****k@g****m 1
Jonas 5****r 1
Kevin Booth k****n@k****g 1
Michele Claus 3****e 1
Ryan Avery r****y@g****m 1
Tyler Erickson t****n@g****m 1
kelseyjosund 5****d 1

Committer domains:

Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 161
Total pull requests: 194
Average time to close issues: 2 months
Average time to close pull requests: 15 days
Total issue authors: 30
Total pull request authors: 22
Average comments per issue: 2.68
Average comments per pull request: 0.84
Merged pull request: 143
Bot issues: 0
Bot pull requests: 5

Past year issues: 43
Past year pull requests: 75
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 6 days
Past year issue authors: 22
Past year pull request authors: 19
Past year average comments per issue: 2.77
Past year average comments per pull request: 0.53
Past year merged pull request: 56
Past year bot issues: 0
Past year bot pull requests: 3

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/clay-foundation/model

Top Issue Authors

  • brunosan (59)
  • yellowcap (48)
  • weiji14 (11)
  • lillythomas (8)
  • robmarkcole (4)
  • MaxLenormand (3)
  • mikeskaug (2)
  • MaceGrim (2)
  • jacobbieker (2)
  • rbavery (2)
  • mattpaul (1)
  • ritwikvashistha (1)
  • mmarks13 (1)
  • bgheneti (1)
  • patriksabol (1)

Top Pull Request Authors

  • yellowcap (63)
  • weiji14 (47)
  • srmsoumya (22)
  • brunosan (20)
  • lillythomas (16)
  • pre-commit-ci[bot] (5)
  • tylere (3)
  • chuckwondo (2)
  • MaxLenormand (2)
  • wboykinm (2)
  • yeelauren (1)
  • kelseyjosund (1)
  • kbgg (1)
  • Latticeworks1 (1)
  • fnands (1)

Top Issue Labels

  • data-pipeline (14)
  • bug (11)
  • benchmark (9)
  • enhancement (6)
  • operational (6)
  • documentation (3)
  • question (3)
  • help wanted (3)
  • maintenance (1)

Top Pull Request Labels

  • data-pipeline (15)
  • documentation (15)
  • maintenance (6)
  • model-architecture (6)
  • enhancement (1)
  • invalid (1)

Package metadata

proxy.golang.org: github.com/clay-foundation/model

proxy.golang.org: github.com/Clay-foundation/model

pypi.org: clay-foundation-model

A PyTorch implementation of MAE (Masked Autoencoder) for satellite imagery

  • Homepage:
  • Documentation: https://clay-foundation-model.readthedocs.io/
  • Licenses: MIT
  • Latest release: 0.1.0 (published 2 months ago)
  • Last Synced: 2025-04-25T12:10:31.802Z (2 days ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 51 Last month
  • Rankings:
    • Dependent packages count: 9.626%
    • Average: 31.913%
    • Dependent repos count: 54.2%
  • Maintainers (1)

Dependencies

.github/workflows/test.yml actions
  • actions/checkout b4ffde65f46336ab88eb53be808477a3936bae11 composite
  • mamba-org/setup-micromamba db1df3ba9e07ea86f759e98b575c002747e9e757 composite
environment.yml pypi
pyproject.toml pypi

Score: 13.039868331873514