Supervised Classification in Google Earth Engine (GEE): A Technical Workflow
For a GIS professional or remote sensing scientist, Google Earth Engine (GEE) has revolutionized how we handle planetary-scale data. Supervised classification is the cornerstone of generating Land Use and Land Cover (LULC) maps. By training a machine learning algorithm on known "ground truth" pixels, we can classify vast satellite image collections (Sentinel-2, Landsat) into thematic maps with high precision.
Here is the standard Super User workflow for executing a supervised classification in the GEE JavaScript API.
1. Data Preparation and Feature Selection
The success of any web application-based classification depends on the quality of the input "Features" (bands and indices). Using raw bands is often insufficient; you should calculate spectral indices to improve class separability.
- Image Collection: Filter your collection by date and location, then create a median or greenest-pixel composite to remove clouds.
- Feature Engineering: Add NDVI (Vegetation), NDBI (Built-up), and MNDWI (Water) as additional bands to your image.
- Normalization: While algorithms like Random Forest are scale-invariant, others like SVM benefit from min-max scaling.
2. Collecting Training Data
In GEE, you create FeatureCollections for each class (e.g., Forest, Urban, Water).
- Use the Geometry tools to drop markers or polygons on the map.
- Ensure each geometry has a 'class' property (e.g., 0, 1, 2).
- Merge: Combine your training sets into one
FeatureCollection:var trainingPoints = forest.merge(urban).merge(water);
3. Sampling and Training the Classifier
Once your training points are ready, you must extract the spectral values from your image at those specific locations.
var training = image.sampleRegions({
collection: trainingPoints,
properties: ['class'],
scale: 10
});
// Train a Random Forest Classifier
var classifier = ee.Classifier.smileRandomForest(100).train({
features: training,
classProperty: 'class',
inputProperties: image.bandNames()
});
4. Accuracy Assessment: Validation is Key
A webmaster or researcher cannot claim a map is accurate without a confusion matrix. You should always split your training data into "Train" (70%) and "Test" (30%) sets.
- Confusion Matrix: Generate a matrix to see where the classifier is misidentifying pixels (e.g., confusing bare soil with urban).
- Kappa Coefficient: A robust metric for measuring agreement beyond chance.
- Overall Accuracy: The percentage of correctly classified pixels in your validation set.
5. SEO and Performance for GEE Apps
If you are deploying your classification as a web application (GEE App), performance directly impacts SEO and user retention.
- Exporting Results: Avoid running heavy classifications on-the-fly for large areas. Export the classified image to a Cloud Asset and load the static result in your app.
- Memory Management: Use
.clip()to restrict processing to your study area, preventing "User Memory Limit Exceeded" errors that would break the web application experience. - Metadata: When sharing results, include structured metadata regarding the satellite source and date to improve Google Search visibility for your research data.
Conclusion
Supervised classification in Google Earth Engine is a powerful iterative process. By selecting the right features, using a robust classifier like Random Forest, and strictly validating your results with independent test data, you can produce professional-grade GIS products. For Super Users, mastering the script-based environment of GEE is the fastest way to turn raw satellite pixels into actionable environmental insights that rank highly in both scientific and search engine optimized contexts.
