Geemap

Abstract

1. Introduction

For video tutorials and notebook examples, please visit the examples page. For complete documentation on geemap modules and methods, please visit the API Reference.

Check out the geemap workshop presented at the GeoPython Conference 2021. This workshop gives a comprehensive introduction to the key features of geemap.

The book _Earth Engine and Geemap: Geospatial Data Science with Python_, written by Qiusheng Wu, has been published by Locate Press in July 2023. If you're interested in purchasing the book, please visit this URL: .

2. Objective

A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

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

3. Key Features / Modules

• Convert Earth Engine JavaScripts to Python scripts and Jupyter notebooks.
• Display Earth Engine data layers for interactive mapping.
• Support Earth Engine JavaScript API-styled functions in Python, such as Map.addLayer(), Map.setCenter(), Map.centerObject(), Map.setOptions().
• Create split-panel maps with Earth Engine data.
• Retrieve Earth Engine data interactively using the Inspector Tool.
• Interactive plotting of Earth Engine data by simply clicking on the map.
• Convert data format between GeoJSON and Earth Engine.
• Use drawing tools to interact with Earth Engine data.
• Use shapefiles with Earth Engine without having to upload data to one's GEE account.
• Export Earth Engine FeatureCollection to other formats (i.e., shp, csv, json, kml, kmz).

4. Technology Stack

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
• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/gee-community/geemap.git
cd geemap

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 gee-community and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.