सूचना: 15 अप्रैल, 2025 से पहले, Earth Engine का इस्तेमाल करने के लिए रजिस्टर किए गए सभी गैर-व्यावसायिक प्रोजेक्ट को, Earth Engine का ऐक्सेस बनाए रखने के लिए, गैर-व्यावसायिक इस्तेमाल की ज़रूरी शर्तों की पुष्टि करनी होगी.
संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
Export.table का इस्तेमाल करके, FeatureCollection को CSV, SHP (शेपफ़ाइल), GeoJSON, KML, KMZ या TFRecord के तौर पर एक्सपोर्ट किया जा सकता है. FeatureCollection, वैक्टर या सिर्फ़ डेटा की टेबल दिखा सकता है. बाद वाले मामले में, कलेक्शन में मौजूद सुविधाओं की ज्यामिति शून्य होगी.
कुछ फ़ाइल फ़ॉर्मैट के साथ काम करते समय, इन अतिरिक्त सीमाओं का ध्यान रखें:
KML: KML फ़ाइल में एक्सपोर्ट किए गए FeatureCollection में, सभी ज्यामिति को बिना प्रोजेक्ट किए गए (WGS84) निर्देशांक में बदल दिया जाएगा.
SHP: शेपफ़ाइल में एक्सपोर्ट किए गए FeatureCollection में, एक ही तरह की ज्यामिति और प्रोजेक्शन वाली सुविधाएं होनी चाहिए. साथ ही, यह शेपफ़ाइल के साइज़ की सीमाओं में फ़िट होनी चाहिए. कॉलम के नाम को 10 या उससे कम वर्णों में काटा जाता है. इससे कॉलम के डुप्लीकेट नाम नहीं बनते.
किसी FeatureCollection को Cloud Storage में एक्सपोर्ट करने के लिए, Export.table.toCloudStorage() का इस्तेमाल करें. उदाहरण के लिए, पहले से तय किए गए features का इस्तेमाल करके:
FeatureCollection को Earth Engine एसेट के तौर पर एक्सपोर्ट करने के लिए, Export.table.toAsset() का इस्तेमाल करें. उदाहरण के लिए, पहले से तय किए गए features का इस्तेमाल करके:
ध्यान दें कि भौगोलिक डेटा को मैनेज करने के लिए, आउटपुट फ़ॉर्मैट को KML के तौर पर सेट किया गया है. ज्यामिति वाली टेबल को एक्सपोर्ट करने के लिए, SHP भी सही होगा. सिर्फ़ डेटा की टेबल को एक्सपोर्ट करने के लिए, CSV फ़ॉर्मैट में, वैल्यू के तौर पर 'शून्य' वाली ज्यामिति वाली सुविधाओं को एक्सपोर्ट करें. यहां, लंबे समय तक चलने वाले ट्रैफ़िक में कमी के नतीजे पाने के लिए, Export.table.toDrive() का इस्तेमाल करने का तरीका बताया गया है:
[null,null,["आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया."],[[["\u003cp\u003e\u003ccode\u003eExport.table\u003c/code\u003e allows you to export \u003ccode\u003eFeatureCollection\u003c/code\u003e data from Earth Engine in various formats like CSV, SHP, GeoJSON, KML, KMZ, and TFRecord, which can represent vector data or simply a table.\u003c/p\u003e\n"],["\u003cp\u003eWhen exporting, be aware of format-specific constraints such as coordinate systems for KML and geometry type/size limits for SHP.\u003c/p\u003e\n"],["\u003cp\u003eYou can export \u003ccode\u003eFeatureCollection\u003c/code\u003e data to your Google Drive, Cloud Storage, or as an Earth Engine asset using designated functions like \u003ccode\u003etoDrive()\u003c/code\u003e, \u003ccode\u003etoCloudStorage()\u003c/code\u003e, and \u003ccode\u003etoAsset()\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eFor exporting data without geographic information, use features with null geometry and export in CSV format, but be mindful of potential data type conversions in Google Drive.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine table assets have limitations on the number of features, properties, geometry vertices, and string value lengths.\u003c/p\u003e\n"]]],[],null,["# Exporting Table and Vector Data\n\nYou can export a `FeatureCollection` as CSV, SHP (shapefile), GeoJSON, KML, KMZ\nor TFRecord using `Export.table`. The `FeatureCollection` may represent vectors\nor simply a table of data. In the latter case, the features in the collection\nwill have null geometry.\n\nNote some additional constraints when working with some file formats, including:\n\n- **KML** : A `FeatureCollection` exported to a KML file will have all the geometries transformed to unprojected (WGS84) coordinates.\n- **SHP** : A `FeatureCollection` exported to a Shapefile must contain features with the same geometry type and projection and must fit within the [Shapefile size\n limits](https://support.esri.com/en/technical-article/000010813). Column names are truncated to 10 characters or fewer, and this must not create duplicate column names.\n- **TFRecord** : See [this page](/earth-engine/guides/tfrecord#exporting-tables).\n\n| **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); })`\n\nto Cloud Storage\n----------------\n\nTo export a `FeatureCollection` to Cloud Storage, use\n`Export.table.toCloudStorage()`. For example, using the `features` defined\npreviously:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Make a collection of points.\nvar features = ee.FeatureCollection([\n ee.Feature(ee.Geometry.Point(30.41, 59.933), {name: 'Voronoi'}),\n ee.Feature(ee.Geometry.Point(-73.96, 40.781), {name: 'Thiessen'}),\n ee.Feature(ee.Geometry.Point(6.4806, 50.8012), {name: 'Dirichlet'})\n]);\n\n// Export a KML file to Cloud Storage.\nExport.table.toCloudStorage({\n collection: features,\n description:'vectorsToCloudStorageExample',\n bucket: 'your-bucket-name',\n fileNamePrefix: 'exampleTableExport',\n fileFormat: 'KML'\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Make a collection of points.\nfeatures = ee.FeatureCollection([\n ee.Feature(ee.Geometry.Point(30.41, 59.933), {'name': 'Voronoi'}),\n ee.Feature(ee.Geometry.Point(-73.96, 40.781), {'name': 'Thiessen'}),\n ee.Feature(ee.Geometry.Point(6.4806, 50.8012), {'name': 'Dirichlet'}),\n])\n\n# Export a KML file to Cloud Storage.\ntask = ee.batch.Export.table.toCloudStorage(\n collection=features,\n description='vectorsToCloudStorageExample',\n bucket='your-bucket-name',\n fileNamePrefix='exampleTableExport',\n fileFormat='KML',\n)\ntask.start()\n```\n\nto Asset\n--------\n\nTo export a `FeatureCollection` as an Earth Engine asset, use\n`Export.table.toAsset()`. For example, using the `features` defined previously:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Export an ee.FeatureCollection as an Earth Engine asset.\nExport.table.toAsset({\n collection: features,\n description:'exportToTableAssetExample',\n assetId: 'exampleAssetId',\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Export an ee.FeatureCollection as an Earth Engine asset.\ntask = ee.batch.Export.table.toAsset(\n collection=features,\n description='exportToTableAssetExample',\n assetId='projects/your-project/assets/exampleAssetId',\n)\ntask.start()\n```\n\nThere are several limitations on the size and shape of Earth Engine table\nassets:\n\n- Maximum of 100 million features\n- Maximum of 1,000 properties (columns)\n- Maximum of 100,000 vertices for each row's geometry\n- Maximum of 100,000 characters per string value\n\nto BigQuery\n-----------\n\nYou can export a `FeatureCollection` to a BigQuery table using\n[`Export.table.toBigQuery()`](/earth-engine/apidocs/export-table-tobigquery).\nThis lets you integrate your Earth Engine data with other data and tools\navailable in BigQuery. For more information, see the\n[Exporting to BigQuery guide](/earth-engine/guides/exporting_to_bigquery).\n\n### Code Editor (JavaScript)\n\n```javascript\nExport.table.toBigQuery({\n collection: features,\n table: 'myproject.mydataset.mytable',\n description: 'put_my_data_in_bigquery',\n append: true,\n overwrite: false\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\ntask = ee.batch.Export.table.toBigQuery(\n collection=features,\n table='myproject.mydataset.mytable',\n description='put_my_data_in_bigquery',\n append=True,\n overwrite=False,\n)\ntask.start()\n```\n\nto Drive\n--------\n\nTo export a `FeatureCollection` to your Drive account, use\n`Export.table.toDrive()`. For example:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Export the FeatureCollection to a KML file.\nExport.table.toDrive({\n collection: features,\n description:'vectorsToDriveExample',\n fileFormat: 'KML'\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Export the FeatureCollection to a KML file.\ntask = ee.batch.Export.table.toDrive(\n collection=features, description='vectorsToDriveExample', fileFormat='KML'\n)\ntask.start()\n```\n\nNote that the output format is specified as KML to handle geographic data (SHP\nwould also be appropriate for exporting a table with geometry). To export just a\ntable of data, without any geographic information, export features with null\ngeometry in CSV format. The following demonstrates using\n`Export.table.toDrive()` to get the results of a potentially long running\nreduction:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a Landsat image.\nvar image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318');\nvar projection = image.select('B2').projection().getInfo();\n\n// Create an arbitrary rectangle.\nvar region = ee.Geometry.Rectangle(-122.2806, 37.1209, -122.0554, 37.2413);\n\n// Get a dictionary of means in the region.\nvar means = image.reduceRegion({\n reducer: ee.Reducer.mean(),\n geometry: region,\n crs: projection.crs,\n crsTransform: projection.transform,\n});\n\n// Make a feature without geometry and set the properties to the dictionary of means.\nvar feature = ee.Feature(null, means);\n\n// Wrap the Feature in a FeatureCollection for export.\nvar featureCollection = ee.FeatureCollection([feature]);\n\n// Export the FeatureCollection.\nExport.table.toDrive({\n collection: featureCollection,\n description: 'exportTableExample',\n fileFormat: 'CSV'\n});\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load a Landsat image.\nimage = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')\nprojection = image.select('B2').projection().getInfo()\n\n# Create an arbitrary rectangle.\nregion = ee.Geometry.Rectangle(-122.2806, 37.1209, -122.0554, 37.2413)\n\n# Get a dictionary of means in the region.\nmeans = image.reduceRegion(\n reducer=ee.Reducer.mean(),\n geometry=region,\n crs=projection['crs'],\n crsTransform=projection['transform'],\n)\n\n# Make a feature without geometry and set the properties to the dictionary of means.\nfeature = ee.Feature(None, means)\n\n# Wrap the Feature in a FeatureCollection for export.\nfeature_collection = ee.FeatureCollection([feature])\n\n# Export the FeatureCollection.\ntask = ee.batch.Export.table.toDrive(\n collection=feature_collection,\n description='exportTableExample',\n fileFormat='CSV',\n)\ntask.start()\n```\n\nNote that the format is set to 'CSV' in this example since there is no geometry\nin the output.\n| **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\n| settings](/earth-engine/faq#tables_exported_to_drive_as_csv_format_are_converted_to_xslx_format)."]]
