vhr-cloudmask.pytorch

Expanding our current cloud masking capabilities using OmniCloudMask

Downloading the Container

All Python and GPU depenencies are installed in an OCI compliant Docker image. You can download this image into a Singularity format to use in HPC systems.

singularity pull docker://nasanccs/vhr-cloudmask.pytorch:latest

In some cases, HPC systems require Singularity containers to be built as sandbox environments because of uid issues (this is the case of NCCS Explore). For that case you can build a sandbox using the following command. Depending the filesystem, this can take between 5 minutes to an hour.

singularity build --sandbox /lscratch/jacaraba/container/vhr-cloudmask.pytorch docker://nasanccs/vhr-cloudmask.pytorch:latest

If you have done this step, you can skip the Installation step since the containers already come with all dependencies installed.

Quickstart

To run inference for all "tif" files in a directory:

singularity shell --nv --env PYTHONPATH=/explore/nobackup/people/$USER/development/VHR-TOOLKIT-FRAMEWORK/vhr-cloudmask.pytorch -B $NOBACKUP,/explore/nobackup/people,/explore/nobackup/projects,/css,/nfs4m /lscratch/$NOBACKUP/container/vhr-cloudmask.pytorch python /explore/nobackup/people/$NOBACKUP/development/VHR-TOOLKIT-FRAMEWORK/vhr-cloudmask.pytorch/vhr_cloudmask/view/cloudmask_cnn_pipeline_cli.py -r '/explore/nobackup/projects/above/misc/ABoVE_Shrubs/srlite/002m/5-toas/*.tif' -o 'vhr-cloudmask-outputs' --overwrite

Authors

• Jordan Alexis Caraballo-Vega, jordan.a.caraballo-vega@nasa.gov

Contributing

Please see our guide for contributing to vhr-cloudmask.pytorch. Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Report Bugs

Report bugs at https://github.com/nasa-nccs-hpda/vhr-cloudmask.pytorch/issues.

If you are reporting a bug, please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

Write Documentation

vhr-cloudmask.pytorch could always use more documentation, whether as part of the official vhr-cloudmask.pytorch docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/nasa-nccs-hpda/vhr-cloudmask.pytorch/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Additional References

[1] Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.

[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, https://github.com/pytorch/pytorch. Accessed 13 February 2020.

[3] Caraballo-Vega, J., Carroll, M., Li, J., & Duffy, D. (2021, December). Towards Scalable & GPU Accelerated Earth Science Imagery Processing: An AI/ML Case Study. In AGU Fall Meeting 2021. AGU.

[4] Jordan Alexis Caraballo-Vega. (2026). nasa-nccs-hpda/vhr-cloudmask.pytorch: 0.1.0 (0.1.0). Zenodo. https://doi.org/10.5281/zenodo.18406518