ee.FeatureCollection.distance

  • This function produces an image where each pixel represents the distance in meters to the nearest geometry within a collection.

  • Distances are computed on a sphere, introducing a small error proportional to the latitude difference.

  • Pixels outside of the specified searchRadius from a geometry will be masked out.

  • The function takes an input FeatureCollection, a searchRadius in meters, and an optional maxError for reprojection.

 Produces a DOUBLE image where each pixel is the distance in meters from the pixel center to the nearest Point, LineString, or polygonal boundary in the collection. Note distance is also measured within interiors of polygons. Pixels that are not within 'searchRadius' meters of a geometry will be masked out.

Distances are computed on a sphere, so there is a small error proportional to the latitude difference between each pixel and the nearest geometry.

UsageReturns
FeatureCollection.distance(searchRadius, maxError)Image
ArgumentTypeDetails
this: featuresFeatureCollectionFeature collection from which to get features used to compute pixel distances.
searchRadiusFloat, default: 100000Maximum distance in meters from each pixel to look for edges. Pixels will be masked unless there are edges within this distance.
maxErrorFloat, default: 100Maximum reprojection error in meters, only used if the input polylines require reprojection. If '0' is provided, then this operation will fail if projection is required.

Examples

Code Editor (JavaScript)

// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
             .filter('country_lg == "Belgium"');

// Generate an image of distance to nearest power plant.
var distance = fc.distance({searchRadius: 50000, maxError: 50});

// Display the image and FeatureCollection on the map.
Map.setCenter(4.56, 50.78, 7);
Map.addLayer(distance, {max: 50000}, 'Distance to power plants');
Map.addLayer(fc, {color: 'red'}, 'Power plants');

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)

# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
    'country_lg == "Belgium"'
)

# Generate an image of distance to nearest power plant.
distance = fc.distance(searchRadius=50000, maxError=50)

# Display the image and FeatureCollection on the map.
m = geemap.Map()
m.set_center(4.56, 50.78, 7)
m.add_layer(distance, {'max': 50000}, 'Distance to power plants')
m.add_layer(fc, {'color': 'red'}, 'Power plants')
m