A collection of 220+ Python examples for using Google Earth Engine in QGIS
Contact: Qiusheng Wu (https://wetlands.io)
This repository is a collection of 220+ Python examples for the Google Earth Engine plugin for QGIS. I developed these Python examples by converting all (except those unsupported by QGIS) the JavaScript examples from the Google Earth Engine API Documentation. Additionally, some examples were adapted from Gena's examples and the Earth Engine API examples. Kudos to Gennadii Donchyts for developing this amazing Google Earth Engine plugin for QGIS.
- Step 1: Sign up for Google Earth Engine.
- Step 2: Install QGIS.
- Step 3: Install the Google Earth Engine Plugin for QGIS and authenticate Google Earth Engine.
- Step 4: Git clone or download this repository.
- Step 5: Open the Python console in QGIS and load any downloaded Python script into the QGIS Python Editor.
- Step 6: Click the Run script button on the Python Editor to execute the script.
- Step 7: Zoom in/out the QGIS Canvas to inspect the results.
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.
- Hello world!
- Adding data to QGIS
- Finding images
- Band math
- Mapping (what to do instead of a for-loop)
- Reducing
- Image statistics
- Masking
- A complete example
- Supervised Classification Algorithms
- Unsupervised Classification Algorithms
- 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
- Extract value to points
- ImageCollection Overview
- ImageCollection Information and Metadata
- Filtering an ImageCollection
- Mapping over an ImageCollection
- Reducing an ImageCollection
- Compositing and Mosaicking
- 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
- Mapping over a FeatureCollection
- Reducing a FeatureCollection
- Vector to Raster Interpolation
- Reducer Overview
- ImageCollection Reductions
- Image Reductions
- Statistics of an Image Region
- Statistics of Image Regions
- Statistics of Image Neighborhoods
- Statistics of FeatureCollection Columns
- Raster to Vector Conversion
- Vector to Raster Conversion
- Grouped Reductions and Zonal Statistics
- Weighted Reductions
- Linear Regression
- Landsat Algorithms
- Sentinel-1 Algorithms
- Resampling and Reducing Resolution
- Linear fit
- Pattern recognition
- Exporting Image
- Exporting ImageCollection
- Exporting Vector
- Exporting FeatureCollection
- Exporting CSV
- Exporting Table
- Exporting TimeSeries