Export.classifier.toAsset

Crée une tâche par lot pour exporter un ee.Classifier en tant qu'élément Earth Engine.

Compatible uniquement avec ee.Classifier.smileRandomForest, ee.Classifier.smileCart, ee.Classifier.DecisionTree et ee.Classifier.DecisionTreeEnsemble.

Utilisation	Renvoie
Export.classifier.toAsset(classifier, description, assetId, priority)

Argument	Type	Détails
classifier	ComputedObject	Classificateur à exporter.
description	Chaîne, facultatif	Nom lisible de la tâche. La valeur par défaut est "myExportClassifierTask".
assetId	Chaîne, facultatif	ID de l'élément de destination.
priority	Numéro (facultatif)	Priorité de la tâche dans le projet. Les tâches à priorité plus élevée sont planifiées plus tôt. Il doit s'agir d'un entier compris entre 0 et 9 999. La valeur par défaut est 100.

Exemples

Éditeur de code (JavaScript)

// First gather the training data for a random forest classifier.
// Let's use MCD12Q1 yearly landcover for the labels.
var landcover = ee.ImageCollection('MODIS/061/MCD12Q1')
    .filterDate('2022-01-01', '2022-12-31')
    .first()
    .select('LC_Type1');
// A region of interest for training our classifier.
var region = ee.Geometry.BBox(17.33, 36.07, 26.13, 43.28);

// Training features will be based on a Landsat 8 composite.
var l8 = ee.ImageCollection('LANDSAT/LC08/C02/T1')
  .filterBounds(region)
    .filterDate('2022-01-01', '2023-01-01');

// Draw the Landsat composite, visualizing true color bands.
var landsatComposite = ee.Algorithms.Landsat.simpleComposite({
  collection: l8,
  asFloat: true
});
Map.addLayer(landsatComposite, {
  min: 0,
  max: 0.3,
  bands: ['B3', 'B2', 'B1']
}, 'Landsat composite');

// Make a training dataset by sampling the stacked images.
var training = landcover.addBands(landsatComposite).sample({
  region: region,
  scale: 30,
  // With export to Classifier we can bump this higher to say 10,000.
  numPixels: 1000
});

var classifier = ee.Classifier.smileRandomForest({
  // We can also increase the number of trees higher to ~100 if needed.
  numberOfTrees: 3
}).train({features: training, classProperty: 'LC_Type1'});

// Create an export classifier task to run.
var assetId = 'projects/<project-name>/assets/<asset-name>';  // <> modify these
Export.classifier.toAsset({
  classifier: classifier,
  description: 'classifier_export',
  assetId: assetId
});

// Load the classifier after the export finishes and visualize.
var savedClassifier = ee.Classifier.load(assetId)
var landcoverPalette = '05450a,086a10,54a708,78d203,009900,c6b044,dcd159,' +
  'dade48,fbff13,b6ff05,27ff87,c24f44,a5a5a5,ff6d4c,69fff8,f9ffa4,1c0dff';
var landcoverVisualization = {
  palette: landcoverPalette,
  min: 0,
  max: 16,
  format: 'png'
};
Map.addLayer(
    landsatComposite.classify(savedClassifier),
    landcoverVisualization,
    'Upsampled landcover, saved');

Configuration de Python

Consultez la page Environnement Python pour en savoir plus sur l'API Python et l'utilisation de geemap pour le développement interactif.

import ee
import geemap.core as geemap

Colab (Python)

# First gather the training data for a random forest classifier.
# Let's use MCD12Q1 yearly landcover for the labels.
landcover = (ee.ImageCollection('MODIS/061/MCD12Q1')
             .filterDate('2022-01-01', '2022-12-31')
             .first()
             .select('LC_Type1'))

# A region of interest for training our classifier.
region = ee.Geometry.BBox(17.33, 36.07, 26.13, 43.28)

# Training features will be based on a Landsat 8 composite.
l8 = (ee.ImageCollection('LANDSAT/LC08/C02/T1')
      .filterBounds(region)
      .filterDate('2022-01-01', '2023-01-01'))

# Draw the Landsat composite, visualizing true color bands.
landsatComposite = ee.Algorithms.Landsat.simpleComposite(
    collection=l8, asFloat=True)

Map = geemap.Map()
Map  # Render the map in the notebook.
Map.addLayer(landsatComposite, {
    'min': 0,
    'max': 0.3,
    'bands': ['B3', 'B2', 'B1']
}, 'Landsat composite')

# Make a training dataset by sampling the stacked images.
training = landcover.addBands(landsatComposite).sample(
    region=region,
    scale=30,
    # With export to Classifier we can bump this higher to say 10,000.
    numPixels=1000
)

# We can also increase the number of trees higher to ~100 if needed.
classifier = ee.Classifier.smileRandomForest(
    numberOfTrees=3).train(features=training, classProperty='LC_Type1')

# Create an export classifier task to run.
asset_id = 'projects/<project-name>/assets/<asset-name>'  # <> modify these
ee.batch.Export.classifier.toAsset(
    classifier=classifier,
    description='classifier_export',
    assetId=asset_id
)

# Load the classifier after the export finishes and visualize.
savedClassifier = ee.Classifier.load(asset_id)
landcover_palette = [
    '05450a', '086a10', '54a708', '78d203', '009900',
    'c6b044', 'dcd159', 'dade48', 'fbff13', 'b6ff05',
    '27ff87', 'c24f44', 'a5a5a5', 'ff6d4c', '69fff8',
    'f9ffa4', '1c0dff']
landcoverVisualization = {
    'palette': landcover_palette,
    'min': 0,
    'max': 16,
    'format': 'png'
}
Map.addLayer(
    landsatComposite.classify(savedClassifier),
    landcoverVisualization,
    'Upsampled landcover, saved')