Add Export.classifier.toAsset to API reference docs.

Also updates ee.Model.fromVertexAi docs.

PiperOrigin-RevId: 589247154

samples/javascript/apidocs/export-classifier-toasset.js

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+/**
+ * Copyright 2023 The Google Earth Engine Community Authors
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// [START earthengine__apidocs__export_classifier_toasset]
+// 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');
+// [END earthengine__apidocs__export_image_toasset]

samples/python/apidocs/export-classifier-toasset.py

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+# Copyright 2023 The Google Earth Engine Community Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import ee
+import geemap.core as geemap
+# [START earthengine__apidocs__export_classifier_toasset]
+# 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')
+# [END earthengine__apidocs__export_classifier_toasset]