Export.classifier.toAsset

Crea un'attività batch per esportare un ee.Classifier come asset Earth Engine.

Supportato solo per ee.Classifier.smileRandomForest, ee.Classifier.smileCart, ee.Classifier.DecisionTree ed ee.Classifier.DecisionTreeEnsemble.

Utilizzo	Resi
Export.classifier.toAsset(classifier, description, assetId, priority)

Argomento	Tipo	Dettagli
classifier	ComputedObject	Il classificatore da esportare.
description	Stringa, facoltativa	Un nome leggibile dell'attività. Il valore predefinito è "myExportClassifierTask".
assetId	Stringa, facoltativa	L'ID della risorsa di destinazione.
priority	Numero (facoltativo)	La priorità dell'attività all'interno del progetto. Le attività con priorità più elevata vengono pianificate prima. Deve essere un numero intero compreso tra 0 e 9999. Il valore predefinito è 100.

Esempi

Editor di codice (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');

Configurazione di Python

Per informazioni sull'API Python e sull'utilizzo di geemap per lo sviluppo interattivo, consulta la pagina Ambiente Python.

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')