AI-generated Key Takeaways
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Remaps property values in a FeatureCollection using parallel lists for input and output mappings.
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Elements with values not found in the input mapping list (
lookupIn
) are excluded from the output. -
Accepts FeatureCollections, input and output mapping lists, and the property name for remapping as arguments.
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Useful for classifying features based on specific property values and simplifying data representation.
Usage | Returns |
---|---|
FeatureCollection.remap(lookupIn, lookupOut, columnName) | FeatureCollection |
Argument | Type | Details |
---|---|---|
this: collection | FeatureCollection | The collection to be modified. |
lookupIn | List | The input mapping values. Restricted to strings and integers. |
lookupOut | List | The output mapping values. Must be the same size as lookupIn. |
columnName | String | The name of the property to remap. |
Examples
Code Editor (JavaScript)
// Classify features based on a string property. // The 'nonsense' category gets dropped. var fc = ee.FeatureCollection([ ee.Feature(ee.Geometry.Point([1, 2]), {isTree: 'Tree'}), ee.Feature(ee.Geometry.Point([3, 4]), {isTree: 'NotTree'}), ee.Feature(ee.Geometry.Point([5, 6]), {isTree: 'nonsense'}), ]); var trees = fc.remap(['NotTree', 'Tree'], [0, 1], 'isTree'); print('remapped trees', trees);
import ee import geemap.core as geemap
Colab (Python)
# Classify features based on a string property. # The 'nonsense' category gets dropped. fc = ee.FeatureCollection([ ee.Feature(ee.Geometry.Point([1, 2]), {'isTree': 'Tree'}), ee.Feature(ee.Geometry.Point([3, 4]), {'isTree': 'NotTree'}), ee.Feature(ee.Geometry.Point([5, 6]), {'isTree': 'nonsense'}), ]) trees = fc.remap(['NotTree', 'Tree'], [0, 1], 'isTree') print('Remapped trees:', trees.getInfo())