ESIPFed / Geoweaver

boost data pipeline's tangibility, enhance research productivity, reduce work anxiety
https://geoweaver.dev
MIT License
75 stars 41 forks source link
ai earth-science esip-lab pipeline pipeline-framework productivity scientific-computing tangibility workflow workflow-engine workflow-management workflow-tool

example workflow License Stars Forks Issues Coverage PyPi Minimum Java Version Geoweaver Docs

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GitHub Repo: https://github.com/ESIPFed/Geoweaver

Pygeoweaver (Python Bindings): https://github.com/ESIPFed/pygeoweaver

Live Demo: https://geobrain.csiss.gmu.edu/Geoweaver

Geoweaver is an in-browser software allowing users to easily compose and execute full-stack data processing workflows via taking advantage of online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It provides all-in-one capacity covering server management, code repository, workflow orchestration software, and history recorder.

It can be run from both local and remote (distributed) machines.

Why choose Geoweaver?

1) Safely Store all your progress along the way. 2) Stay organised and productive through out your years-long research 4) Seamlessly connect to external servers with SSH. 5) In-Built Web UI with full support for Python.

For further insights into Geoweaver, please explore the website at https://geoweaver.dev. GeoWeaver is a community effort. Any contribution is welcome and greatly appreciated!

Features

1) Host Management:

2) Process Variety:

3) Jupyter Notebook Integration:

4) Process History and Logging:

5) Workflow Management:

6) Boosts Data Pipeline's Tangibility:

7) Enhances Research Productivity and Reduces Work Anxiety:

Geoweaver Installation Guide

Geoweaver is a powerful tool for geospatial data processing, offering a range of features and capabilities. This guide will walk you through the steps to install Geoweaver on your system.

Prerequisites

Before you begin, ensure that you have the following dependencies installed:

Demo

A live demo site is available.

Usage

Documentation

Learn more about Geoweaver in its official documentation at https://esipfed.github.io/Geoweaver/docs/install.html

Creating a New Release

For detailed steps on how to create a new release in Geoweaver, please refer to the release instructions.

PyGeoWeaver

PyGeoWeaver is a Python package that provides a convenient and user-friendly interface to interact with GeoWeaver, a powerful geospatial data processing application written in Java. With PyGeoWeaver, Jupyter notebook and JupyterLab users can seamlessly integrate and utilize the capabilities of GeoWeaver within their Python workflows.

Please do visit the PyGeoWeaver GitHub repository.

Contributors

Thanks to our many contributors!

Contributors

Geoweaver History

Version 1.0 (July 2020)

The first stable release of Geoweaver was launched in July 2020 after nearly a year of active development. Key features included:

Despite being an early version, Geoweaver quickly gained traction among GIS enthusiasts and developers due to its modern architecture and extensibility.

Version 2.0 (Upcoming)

The Geoweaver development team is currently working on version 3.0, which is expected to be released in late 2024.

With each new release, the Geoweaver project continues to push the boundaries of open-source GIS software, providing users with a flexible and powerful platform for spatial data management and analysis.

Citation

If you found Geoweaver helpful in your research, please cite:

Sun, Z. et al., "Geoweaver: Advanced cyberinfrastructure for managing hybrid geoscientific AI workflows." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 119.

Existing Projects

Sun, Ziheng, Nicoleta C. Cristea, Kehan Yang, Ahmed Alnuaim, Lakshmi Chetana Gomaram Bikshapathireddy, Aji John, Justin Pflug et al. "Making machine learning-based snow water equivalent forecasting research productive and reusable by Geoweaver." In AGU fall meeting abstracts, vol. 2022, pp. IN23A-04. 2022.

Sun, Ziheng, and Nicoleta Cristea. "Geoweaver for Automating ML-based High Resolution Snow Mapping Workflow." In AGU Fall Meeting Abstracts, vol. 2021, pp. IN11C-07. 2021.

Sun, Ziheng, Liping Di, Jason Tullis, Annie Bryant Burgess, and Andrew Magill. "Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience." In AGU Fall Meeting Abstracts, vol. 2020, pp. IN011-02. 2020.

Sun, Ziheng, Liping Di, Annie Burgess, Jason A. Tullis, and Andrew B. Magill. "Geoweaver: Advanced cyberinfrastructure for managing hybrid geoscientific AI workflows." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 119.

Sun, Ziheng, Liping Di, Jason Tullis, Annie Bryant Burgess, and Andrew Magill. "Geoweaver: Connecting Dots for Artificial Intelligence in Geoscience." In AGU Fall Meeting Abstracts, vol. 2020, pp. IN011-02. 2020.

License

The MIT License (MIT)

Copyright (c) 2015 Chris Kibble

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.