earthengine-py-notebooks

A collection of 300+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping

Contact: Qiusheng Wu (https://wetlands.io)

1. Description

This repository is a collection of 300+ Jupyter Python notebook examples. I developed these examples by converting my other repo qgis-earthengine-examples from Python scripts to Jupyter notebooks. Now you can display Earth Engine data layers interactively in Jupyter notebooks without having to install QGIS. Three Python packages are being used in these examples, including the Earth Engine Python API, folium, and geehydro. The geehydro Python package builds on the folium package and implements several methods for displaying Earth Engine data layers, such as Map.addLayer(), Map.setCenter(), Map.centerObject(), and Map.setOptions().

2. Usage

• Step 1: Explore this repository and open any available Jupyter notebook in your browser (e.g., template.ipynb)
• Step 2: When the selected Jupyter notebook is open, click the Notebook Viewer link to view the interactive map.
• Step 3: If you would like to execute cells in the notebook interactively, you will need to Sign up for a Google Earth Engine account. Then you can click either Run in Google Colab or Run in binder to execute code interactively. This will allow you to add your own Earth Engine Python script.

3. Demo

4. Examples

The Table of Contents below mimics the structure of the Google Earth Engine API Documentation. I strongly encourage you to check out the API Documentation if you need an in-depth explanation of each Python example. Please note that the list below does not include all the Python examples contained in this repository. You are welcome to explore the repository and find more examples to suit your needs.

Get Started

• Hello world!
• Adding data to Map
• Finding images
• Band math
• Mapping (what to do instead of a for-loop)
• Reducing
• Image statistics
• Masking
• A complete example

Machine Learning

• Supervised Classification Algorithms
  • Classification and Regression Trees (CART) | Support Vector Machine (SVM) | Confusion Matrix
• Unsupervised Classification Algorithms
  • KMeans Clustering

Image

• Image Overview
• Image Visualization
• Image information and metadata
• Mathematical operations
• Relational, conditional and Boolean operations
• Convolutions
• Morphological Operations
• Gradients
• Edge detection
• Spectral transformations
• Texture
• Object-based methods
• Cumulative Cost Mapping
• Registering Images
• Miscellaneous
  • Band statistics (min, max, mean, std) | Image statistics by band | | | | | | | | | | | | | | | | | |

ImageCollection

• ImageCollection Overview
• ImageCollection Information and Metadata
• Filtering an ImageCollection
• Mapping over an ImageCollection
• Reducing an ImageCollection
• Compositing and Mosaicking
• Miscellaneous
  • Get image centroid | Convert ImageCollection to Image | Sort by cloud and date | Filtering by metadata | Filtering by band names | Select image by index | Creating monthly imagery

Geometry, Feature, FeatureCollection

• Geometry Overview
• Geodesic vs. Planar Geometries
• Geometry Visualization and Information
• Geometric Operations
• Feature Overview
• FeatureCollection Overview
• Feature and FeatureCollection Visualization
• FeatureCollection Information and Metadata
• [Filtering a FeatureCollection](https://github.com/giswqs/earthengine-py-notebooks/blob/master/Featur