https://github.com/pondib/eocubemlapi

Proof of Concept for Generic ML APIs to process Earth Observation Data Cubes/ Satellite Image Time Series
https://github.com/pondib/eocubemlapi

ml satellite-data

Last synced: about 2 months ago
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Proof of Concept for Generic ML APIs to process Earth Observation Data Cubes/ Satellite Image Time Series

• Host: GitHub
• URL: https://github.com/pondib/eocubemlapi
• Owner: PondiB
• License: apache-2.0
• Created: 2024-01-30T15:42:11.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-20T21:34:33.000Z (11 months ago)
• Last Synced: 2024-02-20T22:37:32.882Z (11 months ago)
• Topics: ml, satellite-data
• Language: Jupyter Notebook
• Homepage:
• Size: 913 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# EOCubeMLAPI: Generic ML and DL APIs for Earth Observation Data Cubes (Work in Progress)

## Introduction

This repository hosts the Proof of Concept (PoC) leading to Generic Machine Learning (ML) and Deep Learning (DL) APIs designed specifically for processing Earth Observation (EO) Data Cubes/Satellite Image Time Series. Our primary goal is to advance interoperability and reproducibility in the analysis of EO data cubes, particularly in cloud-based environments. This project aims to provide a standardized, easy-to-use interface for applying advanced ML and DL techniques to EO data, facilitating research and applications in fields such as biodiversity monitoring, land use analysis, and crop yield monitoring.

![EOCubeMLAPI Idea ](docs/eo-ml.png)

## Target Features

- **Unified API Structure**: A consistent and intuitive API design that allows users to apply various ML and DL models to EO data cubes without needing extensive knowledge about the underlying data structures.

- **Cloud Optimization**: Designed for efficient performance in cloud computing environments, enabling scalable analysis of large EO datasets.

- **Interoperability**: Seamless integration with existing EO data cube standards and cloud services, ensuring compatibility with a wide range of data sources and processing tools.

- **Reproducibility**: Emphasis on reproducible research practices, with comprehensive documentation and version control of both data and analysis pipelines.

- **Modular Design**: Flexibility to add new ML and DL models, as well as compatibility with existing ML libraries and frameworks.

- **Community-Driven**: Open-source development, encouraging contributions and feedback from the EO ML/DL communities.

## Getting Started

### Prerequisites

Before you begin, ensure you have met the following requirements:

- Python 3.8 or later

- Familiarity with basic concepts of ML/DL and EO data

### Installation

TO DO

### Documentation

Developer Guide: Instructions for contributing to the project.

Contributing

Contributions are welcome! Please read our [Contributing Guidelines](CONTRIBUTING.md)for details on the process for submitting pull requests to us.

## License

This project is licensed under the Apache License Version 2.0 - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

Organizations and projects that provided data and tools.

Contributors and community members who participated in discussions and development.

Contact

For questions and feedback, please contact the project maintainer at [[email protected]](mailto:[email protected]).

## Project Status

This project is currently in the exploration stage and under active development.