Exporting Table and Vector Data

You can export a FeatureCollection as CSV, SHP (shapefile), GeoJSON, KML, KMZ or TFRecord using Export.table. The FeatureCollection may represent vectors or simply a table of data. In the latter case, the features in the collection will have null geometry.

Note some additional constraints when working with some file formats, including:

• KML: A FeatureCollection exported to a KML file will have all the geometries transformed to unprojected (WGS84) coordinates.
• SHP: A FeatureCollection exported to a Shapefile must contain features with the same geometry type and projection and must fit within the Shapefile size limits. Column names are truncated to 10 characters or fewer, and this must not create duplicate column names.
• TFRecord: See this page.

Note: If you need control over the precision of geometries in your export, map() a function over the collection to be exported: map(function(f) { return f.transform(targetProj, maxErr); })

to Cloud Storage

To export a FeatureCollection to Cloud Storage, use Export.table.toCloudStorage(). For example, using the features defined previously:

Code Editor (JavaScript)

// Make a collection of points.
var features = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Point(30.41, 59.933), {name: 'Voronoi'}),
  ee.Feature(ee.Geometry.Point(-73.96, 40.781), {name: 'Thiessen'}),
  ee.Feature(ee.Geometry.Point(6.4806, 50.8012), {name: 'Dirichlet'})
]);

// Export a KML file to Cloud Storage.
Export.table.toCloudStorage({
  collection: features,
  description:'vectorsToCloudStorageExample',
  bucket: 'your-bucket-name',
  fileNamePrefix: 'exampleTableExport',
  fileFormat: 'KML'
});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Make a collection of points.
features = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point(30.41, 59.933), {'name': 'Voronoi'}),
    ee.Feature(ee.Geometry.Point(-73.96, 40.781), {'name': 'Thiessen'}),
    ee.Feature(ee.Geometry.Point(6.4806, 50.8012), {'name': 'Dirichlet'}),
])

# Export a KML file to Cloud Storage.
task = ee.batch.Export.table.toCloudStorage(
    collection=features,
    description='vectorsToCloudStorageExample',
    bucket='your-bucket-name',
    fileNamePrefix='exampleTableExport',
    fileFormat='KML',
)
task.start()

to Asset

To export a FeatureCollection as an Earth Engine asset, use Export.table.toAsset(). For example, using the features defined previously:

Code Editor (JavaScript)

// Export an ee.FeatureCollection as an Earth Engine asset.
Export.table.toAsset({
  collection: features,
  description:'exportToTableAssetExample',
  assetId: 'exampleAssetId',
});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Export an ee.FeatureCollection as an Earth Engine asset.
task = ee.batch.Export.table.toAsset(
    collection=features,
    description='exportToTableAssetExample',
    assetId='projects/your-project/assets/exampleAssetId',
)
task.start()

There are several limitations on the size and shape of Earth Engine table assets:

• Maximum of 100 million features
• Maximum of 1,000 properties (columns)
• Maximum of 100,000 vertices for each row's geometry
• Maximum of 100,000 characters per string value

to BigQuery

You can export a FeatureCollection to a BigQuery table using Export.table.toBigQuery(). This lets you integrate your Earth Engine data with other data and tools available in BigQuery. For more information, see the Exporting to BigQuery guide.

Code Editor (JavaScript)

Export.table.toBigQuery({
  collection: features,
  table: 'myproject.mydataset.mytable',
  description: 'put_my_data_in_bigquery',
  append: true,
  overwrite: false
});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

task = ee.batch.Export.table.toBigQuery(
    collection=features,
    table='myproject.mydataset.mytable',
    description='put_my_data_in_bigquery',
    append=True,
    overwrite=False,
)
task.start()

to Drive

To export a FeatureCollection to your Drive account, use Export.table.toDrive(). For example:

Code Editor (JavaScript)

// Export the FeatureCollection to a KML file.
Export.table.toDrive({
  collection: features,
  description:'vectorsToDriveExample',
  fileFormat: 'KML'
});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Export the FeatureCollection to a KML file.
task = ee.batch.Export.table.toDrive(
    collection=features, description='vectorsToDriveExample', fileFormat='KML'
)
task.start()

Note that the output format is specified as KML to handle geographic data (SHP would also be appropriate for exporting a table with geometry). To export just a table of data, without any geographic information, export features with null geometry in CSV format. The following demonstrates using Export.table.toDrive() to get the results of a potentially long running reduction:

Code Editor (JavaScript)

// Load a Landsat image.
var image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318');
var projection = image.select('B2').projection().getInfo();

// Create an arbitrary rectangle.
var region = ee.Geometry.Rectangle(-122.2806, 37.1209, -122.0554, 37.2413);

// Get a dictionary of means in the region.
var means = image.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: region,
  crs: projection.crs,
  crsTransform: projection.transform,
});

// Make a feature without geometry and set the properties to the dictionary of means.
var feature = ee.Feature(null, means);

// Wrap the Feature in a FeatureCollection for export.
var featureCollection = ee.FeatureCollection([feature]);

// Export the FeatureCollection.
Export.table.toDrive({
  collection: featureCollection,
  description: 'exportTableExample',
  fileFormat: 'CSV'
});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Load a Landsat image.
image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')
projection = image.select('B2').projection().getInfo()

# Create an arbitrary rectangle.
region = ee.Geometry.Rectangle(-122.2806, 37.1209, -122.0554, 37.2413)

# Get a dictionary of means in the region.
means = image.reduceRegion(
    reducer=ee.Reducer.mean(),
    geometry=region,
    crs=projection['crs'],
    crsTransform=projection['transform'],
)

# Make a feature without geometry and set the properties to the dictionary of means.
feature = ee.Feature(None, means)

# Wrap the Feature in a FeatureCollection for export.
feature_collection = ee.FeatureCollection([feature])

# Export the FeatureCollection.
task = ee.batch.Export.table.toDrive(
    collection=feature_collection,
    description='exportTableExample',
    fileFormat='CSV',
)
task.start()

Note that the format is set to 'CSV' in this example since there is no geometry in the output.

Caution: Depending on your Google Drive settings, CSV tables that you export from Earth Engine can be converted to XSLX files with unintended effects, such as data type conversions. The behavior can be modified with Google Drive settings.