Export.classifier.toAsset

Membuat tugas batch untuk mengekspor ee.Classifier sebagai aset Earth Engine.

Hanya didukung untuk ee.Classifier.smileRandomForest, ee.Classifier.smileCart, ee.Classifier.DecisionTree, dan ee.Classifier.DecisionTreeEnsemble.

Penggunaan	Hasil
Export.classifier.toAsset(classifier, description, assetId, priority)

Argumen	Jenis	Detail
classifier	ComputedObject	Pengklasifikasi yang akan diekspor.
description	String, opsional	Nama tugas yang dapat dibaca manusia. Defaultnya adalah "myExportClassifierTask".
assetId	String, opsional	ID aset tujuan.
priority	Angka, opsional	Prioritas tugas dalam project. Tugas dengan prioritas lebih tinggi akan dijadwalkan lebih cepat. Harus berupa bilangan bulat antara 0 dan 9999. Setelan defaultnya adalah 100.

Contoh

Editor Kode (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');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

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')