Eodal

Earth Observation Data Analysis Library

Data SciencePythonGPL-3.0

Abstract

Eodal is an open-source Data Science project. Earth Observation Data Analysis Library. Edal is a Python library enabling the acquisition, organization, and analysis of EO data in a completely open-source manner within a unified framework. It is built using Python. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Edal is a Python library enabling the acquisition, organization, and analysis of EO data in a completely open-source manner within a unified framework.

Edal enables open-source, reproducible geo-spatial data science. At the same time, Edal lowers the burden of data handling and provides access to global satellite data archives through downloaders and the fantastic SpatioTemporalAssetsCatalogs (STAC).

Edal supports working in cloud-environments using STAC catalogs ("online" mode) and on local premises using a spatial PostgreSQL/PostGIS database to organize metadata ("offline" mode).

2. Objective

Earth Observation Data Analysis Library

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

Python

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/EOA-team/eodal.git
cd eodal

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by EOA-team and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

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