ee.Classifier.amnhMaxent

Creates a Maximum Entropy classifier. Maxent is used to model species distribution probabilities using environmental data for locations of known presence and for a large number of 'background' locations. For more information and to cite, see: https://biodiversityinformatics.amnh.org/open_source/maxent/ and the reference publication: Phillips, et. al., 2004 A maximum entropy approach to species distribution modeling, Proceedings of the Twenty-First International Conference on Machine Learning. The output is a single band named 'probability', containing the modeled probability, and an additional band named 'clamp' when the 'writeClampGrid' argument is true.

Usage	Returns
`ee.Classifier.amnhMaxent(categoricalNames, outputFormat, autoFeature, linear, quadratic, product, threshold, hinge, hingeThreshold, l2lqThreshold, lq2lqptThreshold, addSamplesToBackground, addAllSamplesToBackground, betaMultiplier, betaHinge, betaLqp, betaCategorical, betaThreshold, extrapolate, doClamp, writeClampGrid, randomTestPoints, seed)`	Classifier

Argument	Type	Details
`categoricalNames`	List, default: null	A list of the names of the categorical inputs. Any inputs not listed in this argument are considered to be continuous.
`outputFormat`	String, default: "cloglog"	Representation of probabilities in output.
`autoFeature`	Boolean, default: true	Automatically select which feature classes to use, based on number of training samples.
`linear`	Boolean, default: true	Allow linear features to be used. Ignored when autofeature is true.
`quadratic`	Boolean, default: true	Allow quadratic features to be used. Ignored when autofeature is true.
`product`	Boolean, default: true	Allow product features to be used. Ignored when autofeature is true.
`threshold`	Boolean, default: false	Allow threshold features to be used. Ignored when autofeature is true.
`hinge`	Boolean, default: true	Allow hinge features to be used. Ignored when autofeature is true.
`hingeThreshold`	Integer, default: 15	Number of samples at which hinge features start being used. Ignored when autofeature is false.
`l2lqThreshold`	Integer, default: 10	Number of samples at which quadratic features start being used. Ignored when autofeature is false.
`lq2lqptThreshold`	Integer, default: 80	Number of samples at which product and threshold features start being used. Ignored when autofeature is false.
`addSamplesToBackground`	Boolean, default: true	Add to the background any sample for which has a combination of environmental values that isn't already present in the background.
`addAllSamplesToBackground`	Boolean, default: false	Add all samples to the background, even if they have combinations of environmental values that are already present in the background.
`betaMultiplier`	Float, default: 1	Regularization multiplier. Multiply all automatic regularization parameters by this number. A higher number gives a more spread-out distribution.
`betaHinge`	Float, default: -1	Regularization parameter to be applied to all hinge features; negative value enables automatic setting.
`betaLqp`	Float, default: -1	Regularization parameter to be applied to all linear, quadratic and product features; negative value enables automatic setting.
`betaCategorical`	Float, default: -1	Regularization parameter to be applied to all categorical features; negative value enables automatic setting.
`betaThreshold`	Float, default: -1	Regularization parameter to be applied to all threshold features; negative value enables automatic setting.
`extrapolate`	Boolean, default: true	Extrapolate. Predict to regions of environmental space outside the limits encountered during training.
`doClamp`	Boolean, default: true	Apply clamping to output.
`writeClampGrid`	Boolean, default: true	Adds a band to the output ('clamp') showing the spatial distribution of clamping. At each point, the value is the absolute difference between prediction values with and without clamping.
`randomTestPoints`	Integer, default: 0	Random test percentage. The percentage of training points to hold aside as test points, used to compute AUX, omission, etc.
`seed`	Long, default: 0	A seed used when generating random numbers.

Examples

Code Editor (JavaScript)

// Create some sample species presence/absence training data.
var trainingData = ee.FeatureCollection([
  // Species present points.
  ee.Feature(ee.Geometry.Point([-122.39567, 38.02740]), {presence: 1}),
  ee.Feature(ee.Geometry.Point([-122.68560, 37.83690]), {presence: 1}),
  // Species absent points.
  ee.Feature(ee.Geometry.Point([-122.59755, 37.92402]), {presence: 0}),
  ee.Feature(ee.Geometry.Point([-122.47137, 37.99291]), {presence: 0}),
  ee.Feature(ee.Geometry.Point([-122.52905, 37.85642]), {presence: 0}),
  ee.Feature(ee.Geometry.Point([-122.03010, 37.66660]), {presence: 0})
]);

// Import a Landsat 8 surface reflectance image.
var image = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20200606')
                // Select the optical and thermal bands.
                .select(['.._B.*']);

// Sample the image at the location of the points.
var training = image.sampleRegions({collection: trainingData, scale: 30});

// Define and train a Maxent classifier from the image-sampled points.
var classifier = ee.Classifier.amnhMaxent().train({
  features: training,
  classProperty: 'presence',
  inputProperties: image.bandNames()
});

// Classify the image using the Maxent classifier.
var imageClassified = image.classify(classifier);

// Display the layers on the map.
// Species presence probability [0, 1] grades from black to white.
Map.centerObject(image, 9);
Map.addLayer(
    image.select(['SR_B4', 'SR_B3', 'SR_B2']).multiply(0.0000275).add(-0.2),
    {min: 0, max: 0.3}, 'Image');
Map.addLayer(
    imageClassified, {bands: 'probability', min: 0, max: 1}, 'Probability');
Map.addLayer(
    trainingData.filter('presence == 0'), {color: 'red'},
    'Training data (species absent)');
Map.addLayer(
    trainingData.filter('presence == 1'), {color: 'blue'},
    'Training data (species present)');

Colab (Python)

"""Demonstrates the ee.Classifier.amnhMaxent method."""

import ee


# Authenticates to the Earth Engine servers.
ee.Authenticate()
# Initializes the client library.
ee.Initialize()


# Create some sample species presence/absence training data.
training_data = ee.FeatureCollection([
    # Species present points.
    ee.Feature(ee.Geometry.Point([-122.39567, 38.02740]), {'presence': 1}),
    ee.Feature(ee.Geometry.Point([-122.68560, 37.83690]), {'presence': 1}),
    # Species absent points.
    ee.Feature(ee.Geometry.Point([-122.59755, 37.92402]), {'presence': 0}),
    ee.Feature(ee.Geometry.Point([-122.47137, 37.99291]), {'presence': 0}),
    ee.Feature(ee.Geometry.Point([-122.52905, 37.85642]), {'presence': 0}),
    ee.Feature(ee.Geometry.Point([-122.03010, 37.66660]), {'presence': 0})
])

# Import a Landsat 8 image and select the reflectance bands.
image = (ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20200606')
         .select(['SR_B[1-7]'])
         .multiply(0.0000275).add(-0.2))  # Apply scaling factors.

# Sample the image at the location of the points.
training = image.sampleRegions(**{
    'collection': training_data,
    'scale': 30
})

# Define and train a Maxent classifier from the image-sampled points.
classifier = ee.Classifier.amnhMaxent().train(**{
    'features': training,
    'classProperty': 'presence',
    'inputProperties': image.bandNames()
})

# Classify the image using the Maxent classifier.
image_classified = image.classify(classifier)