ee.Kernel.manhattan

  • ee.Kernel.manhattan generates a distance kernel based on rectilinear (city-block) distance.

  • The function takes arguments for radius, optional units, normalize, and magnitude.

  • Examples in JavaScript and Python demonstrate how to create and print a Manhattan kernel with a radius of 3.

 Generates a distance kernel based on rectilinear (city-block) distance.

UsageReturns
ee.Kernel.manhattan(radius, units, normalize, magnitude)Kernel
ArgumentTypeDetails
radiusFloatThe radius of the kernel to generate.
unitsString, default: "pixels"The system of measurement for the kernel ('pixels' or 'meters'). If the kernel is specified in meters, it will resize when the zoom-level is changed.
normalizeBoolean, default: falseNormalize the kernel values to sum to 1.
magnitudeFloat, default: 1Scale each value by this amount.

Examples

Code Editor (JavaScript)

print('A Manhattan kernel', ee.Kernel.manhattan({radius: 3}));

/**
 * Output weights matrix
 *
 * [6, 5, 4, 3, 4, 5, 6]
 * [5, 4, 3, 2, 3, 4, 5]
 * [4, 3, 2, 1, 2, 3, 4]
 * [3, 2, 1, 0, 1, 2, 3]
 * [4, 3, 2, 1, 2, 3, 4]
 * [5, 4, 3, 2, 3, 4, 5]
 * [6, 5, 4, 3, 4, 5, 6]
 */

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)

display('A Manhattan kernel:',ee.Kernel.manhattan(**{'radius': 3}))

#  Output weights matrix

#  [6, 5, 4, 3, 4, 5, 6]
#  [5, 4, 3, 2, 3, 4, 5]
#  [4, 3, 2, 1, 2, 3, 4]
#  [3, 2, 1, 0, 1, 2, 3]
#  [4, 3, 2, 1, 2, 3, 4]
#  [5, 4, 3, 2, 3, 4, 5]
#  [6, 5, 4, 3, 4, 5, 6]