ee.Kernel.manhattan

AI-generated Key Takeaways

• 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.

Usage	Returns
ee.Kernel.manhattan(radius, units, normalize, magnitude)	Kernel

Argument	Type	Details
radius	Float	The radius of the kernel to generate.
units	String, 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.
normalize	Boolean, default: false	Normalize the kernel values to sum to 1.
magnitude	Float, default: 1	Scale 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)

from pprint import pprint

print('A Manhattan kernel:')
pprint(ee.Kernel.manhattan(**{'radius': 3}).getInfo())

#  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]