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)');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

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)