AI-generated Key Takeaways
-
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
, andworkloadTag
. -
Supported output formats for tabular data include
PANDAS_DATAFRAME
andGEOPANDAS_GEODATAFRAME
.
Returns: A list of GeoJSON features reprojected to EPSG:4326 with planar edges.
Usage | Returns |
---|---|
ee.data.computeFeatures(params) | List |
Argument | Type | Details |
---|---|---|
params | Object | An 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
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...