AI-generated Key Takeaways
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Maxent is a Maximum Entropy classifier used for modeling species distribution probabilities based on environmental data from known presence and background locations.
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The classifier outputs a 'probability' band representing the modeled probability, and optionally a 'clamp' band indicating the spatial distribution of clamping.
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The
ee.Classifier.amnhMaxentmethod takes numerous arguments to control various aspects of the model, including feature types, regularization, and output format.
| 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)' );
import ee import geemap.core as geemap
Colab (Python)
"""Demonstrates the ee.Classifier.amnhMaxent method.""" # 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 )
