AI-generated Key Takeaways
-
ee.Kernel.manhattangenerates a distance kernel based on rectilinear (city-block) distance. -
The function takes arguments for
radius, optionalunits,normalize, andmagnitude. -
Examples in JavaScript and Python demonstrate how to create and print a Manhattan kernel with a radius of 3.
| 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] */
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]
