ee.data.computeFeatures (Python only)

  • The ee.data.computeFeatures function computes a list of features by applying a computation to features.

  • It returns a list of GeoJSON features reprojected to EPSG:4326 with planar edges.

  • The function takes an object of parameters which can include expression, pageSize, fileFormat, pageToken, and workloadTag.

  • Supported output formats for tabular data include PANDAS_DATAFRAME and GEOPANDAS_GEODATAFRAME.

Computes a list of features by applying a computation to features.

Returns: A list of GeoJSON features reprojected to EPSG:4326 with planar edges.

UsageReturns
ee.data.computeFeatures(params)List
ArgumentTypeDetails
paramsObjectAn object containing parameters with the following possible values:
expression - The expression to compute.
pageSize - The maximum number of results per page. The server may return fewer images than requested. If unspecified, the page size default is 1000 results per page.
fileFormat - If present, specifies an output format for the tabular data. The function makes a network request for each page until the entire table has been fetched. The number of fetches depends on the number of rows in the table and pageSize. pageToken is ignored. Supported formats are: PANDAS_DATAFRAME for a Pandas DataFrame and GEOPANDAS_GEODATAFRAME for a GeoPandas GeoDataFrame.
pageToken - A token identifying a page of results the server should return.
workloadTag - User supplied tag to track this computation.

Examples

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)

from pprint import pprint

# Region of interest.
pt = ee.Geometry.Point([-122.0679107870136, 36.983302098145906])
# Imagery of interest.
images = (ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
          .filterBounds(pt).filterDate('2021-01-01', '2021-12-31'))

def point_overlay(image):
  """Extracts image band values for pixel-point intersection."""
  return ee.Feature(pt, image.reduceRegion('first', pt, 30))

# Convert an ImageCollection to a FeatureCollection.
features = images.map(point_overlay)

features_dict = ee.data.computeFeatures({'expression': features})

pprint(features_dict)
# Do something with the features...