AI-generated Key Takeaways
-
A FeatureCollection can be exported as CSV, SHP, GeoJSON, KML, KMZ or TFRecord using Export.table.
-
Specific file formats like KML and SHP have constraints on geometry and data.
-
FeatureCollections can be exported to Cloud Storage, Earth Engine assets, BigQuery, or Google Drive.
-
Exporting to Drive allows for various file formats, including CSV for data without geometry.
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
FeatureCollectionexported to a KML file will have all the geometries transformed to unprojected (WGS84) coordinates. - SHP: A
FeatureCollectionexported 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 .
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' });
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' , });
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 });
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' });
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' });
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.
