https://github.com/csaybar/EEwPython

A series of Jupyter notebook to learn Google Earth Engine with Python
https://github.com/csaybar/EEwPython

gee google-earth-engine r

Last synced: about 1 month ago
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A series of Jupyter notebook to learn Google Earth Engine with Python

• Host: GitHub
• URL: https://github.com/csaybar/EEwPython
• Owner: csaybar
• Created: 2018-11-20T23:14:43.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-25T13:20:22.000Z (over 1 year ago)
• Last Synced: 2024-02-07T20:40:11.934Z (5 months ago)
• Topics: gee, google-earth-engine, r
• Language: Jupyter Notebook
• Homepage:
• Size: 9.42 MB
• Stars: 253
• Watchers: 16
• Forks: 160
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

Lists

• awesome-earthobservation-code - EEwPython - A series of Jupyter notebook to learn Google Earth Engine with `Python` (Earth Engine / Testing your code)
• Awesome-GEE - csaybar/EEwPython

README

        
# Google Earth Engine with Python






Maintainer:

- [*`Cesar Aybar Camacho`*](https://csaybar.github.io)  < [email protected] >

- [*`Roy Yali Samaniego`*]()  < [email protected] >



**Welcome!**

The course **"EEwPython"** is a series of Jupyter notebook (colabs) to learn Google Earth Engine (GEE) with python. EEwPython is structured in two parts. The first one is an adaptation from all [Google Earth Engine Documentation](https://developers.google.com/earth-engine/) to be able to run in python, and the second one is a recompilation of different reproducible examples. If you want to contribute with EEwPython, do not doubt to keep in touch with us. All the material is released under the Apache 2.0 license.

## Table of Contents

### 1. Google Earth Engine Guides

- [1. Developer's Guide](1_Introduction.ipynb)

- [2. Image](2_eeImage.ipynb)

- [3. ImageCollection](3_eeImageCollection.ipynb)

- [4. Geometry, Feature & FeatureCollections](4_features.ipynb)

- [5. Reducer](5_Reducer.ipynb)

- [6. Joins](6_Joins.ipynb)

- [7. Chart](7_Chart.ipynb)

- [8. Array](8_Array.ipynb)

- [9. Specialized Algorithms](9_SpecializedAlgorithms.ipynb)

- [10. Export data](10_Export.ipynb)

### 2. Applications

- [1. Climate Change - CMIP5 (plotly + GEE)](cmip5.ipynb)

- [2. Crop Area estimation in Camana Valley using a Deep Neural Network (tensorflow)](dnn_demo.ipynb)

- [3. U-Net + EarthEngine](cnn_demo.ipynb)