Page Summary
-
The
ee.Clustererpackage in Earth Engine is used for unsupervised classification or clustering and is based on algorithms from Weka. -
The general workflow for clustering involves assembling features, instantiating and training a clusterer, and applying it to an image or feature collection.
-
Training data for a clusterer is a
FeatureCollectionwith properties, but unlike classifiers, it doesn't have an input class value. -
When applied, a trained clusterer assigns an integer cluster ID to each pixel or feature.
The ee.Clusterer
package handles unsupervised classification (or clustering
) in Earth Engine. These algorithms are currently based on the
algorithms with the same name in Weka
.
More details about each Clusterer
are available in the reference docs
.
Clusterers are used in the same manner as classifiers in Earth Engine. The general workflow for clustering is:
- Assemble features with numeric properties in which to find clusters.
- Instantiate a clusterer. Set its parameters if necessary.
- Train the clusterer using the training data.
- Apply the clusterer to an image or feature collection.
- Label the clusters.
The training data is a FeatureCollection
with properties that will be
input to the clusterer. Unlike classifiers, there is no input class value for an Clusterer
. Like classifiers, the data for the train and apply steps are
expected to have the same number of values. When a trained clusterer is applied to an image
or table, it assigns an integer cluster ID to each pixel or feature.
Here is a simple example of building and using an ee.Clusterer
:
Code Editor (JavaScript)
// Define a region in which to generate a segmented map. var region = ee . Geometry . Rectangle ( 29.7 , 30 , 32.5 , 31.7 ); // Load a Landsat composite for input. var input = ee . ImageCollection ( 'LANDSAT/COMPOSITES/C02/T1_L2_32DAY' ) . filterDate ( '2001-05' , '2001-06' ) . first () . clip ( region ); // Display the sample region. Map . setCenter ( 31.5 , 31.0 , 8 ); Map . addLayer ( ee . Image (). paint ( region , 0 , 2 ), {}, 'region' ); // Make the training dataset. var training = input . sample ({ region : region , scale : 30 , numPixels : 5000 }); // Instantiate the clusterer and train it. var clusterer = ee . Clusterer . wekaKMeans ( 15 ). train ( training ); // Cluster the input using the trained clusterer. var result = input . cluster ( clusterer ); // Display the clusters with random colors. Map . addLayer ( result . randomVisualizer (), {}, 'clusters' );
import ee import geemap.core as geemap
Colab (Python)
# Define a region in which to generate a segmented map. region = ee . Geometry . Rectangle ( 29.7 , 30 , 32.5 , 31.7 ) # Load a Landsat composite for input. input = ( ee . ImageCollection ( 'LANDSAT/COMPOSITES/C02/T1_L2_32DAY' ) . filterDate ( '2001-05' , '2001-06' ) . first () . clip ( region ) ) # Display the sample region. m = geemap . Map () m . set_center ( 31.5 , 31.0 , 8 ) m . add_layer ( ee . Image () . paint ( region , 0 , 2 ), {}, 'region' ) # Make the training dataset. training = input . sample ( region = region , scale = 30 , numPixels = 5000 ) # Instantiate the clusterer and train it. clusterer = ee . Clusterer . wekaKMeans ( 15 ) . train ( training ) # Cluster the input using the trained clusterer. result = input . cluster ( clusterer ) # Display the clusters with random colors. m . add_layer ( result . randomVisualizer (), {}, 'clusters' ) m
Please note:
- The same inputs should always produce the same outputs, but reordering the inputs can change the results.
- Training with as few as 10 bands * 100k points can produce an Out Of Memory error.
- Cobweb can take a long time to finish and can produce a large number of clusters.
- The output clusters and their IDs are dependent on the algorithm and inputs.
