Google Earth Engine Python API Examples
A collection of Jupyter Notebooks for Google Earth Engine Python API.
You have to change the code in authorize_ee.ipynb
to make it run. Unfortunately this changed with the new version of the GEE Python API. I'll fix it as soon as I have time for it.
Note: If you are interested in more tutorials or code snippets for Google Earth Engine Python API, feel free to write me an email to renekope@gmail.com.
Jupyter Notebook Tutorials for Google Earth Engine
001 Landcover Classfication for Landsat 8 TOA imagery
Classification Example for Landsat 8 including several vegetation indices and object feature extraction. Eventually you can't access the training data. In case you are interested in the training data, feel free to contact me.
002 Tasseled Cap Transformation for Landsat 8 TOA imagery
Tasseled Cap Transformation for Landsat 8 TOA imagery based on the scientfic work "Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance" by M.Baigab, L.Zhang, T.Shuai & Q.Tong (2014).
003 Display Proba-V NDVI Imagery
Display Proba-V NDVI (Normalized Difference Vegetation Index) Imagery.
004 Display Proba-V Time-Series
Display Proba-V NDVI Time-Series using Pandas and Matplotlib.
005 Basic Proba-V Time-Series Analysis
Basic Proba-V NDVI Time-Series Analysis, including auto correlation, fast fourier transformation and outlier detection.
006 Basic Proba-V Time-Series Prediction
Basic Proba-V NDVI Time-Series Prediction, using Fourier extrapolation and ARIMA model.
007 Linear Regression
Linear regression on Proba-V, Landsat and Climate Hazards Group InfraRed Precipitation (CHRIPS) data. This tutorial demonstrates the comparison of one of the most common supervised machine learning methods, the linear regression. We are going to compare scikit-learn and Statsmodels. For more information about types of Machine Learning, check this link.