[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eGenerates a distance kernel based on the rectilinear (city-block) distance, also known as the Manhattan distance.\u003c/p\u003e\n"],["\u003cp\u003eThe kernel can be customized using parameters such as radius, units (pixels or meters), normalization, and magnitude scaling.\u003c/p\u003e\n"],["\u003cp\u003eBy default, the kernel uses pixels as units and is not normalized, with a magnitude of 1.\u003c/p\u003e\n"],["\u003cp\u003eThe output is a square matrix of weights representing the distances from the center pixel, as illustrated in the provided examples.\u003c/p\u003e\n"],["\u003cp\u003eThis kernel is commonly used in image processing for operations like edge detection and feature extraction, where rectilinear distances are relevant.\u003c/p\u003e\n"]]],["This tool generates a rectilinear (city-block) distance kernel using `ee.Kernel.manhattan`. Key actions involve setting the `radius`, specifying `units` as pixels or meters, and optionally `normalize` the kernel to sum to 1, and `magnitude` to scale each value. The kernel's output is a matrix, where each cell's value represents its distance.\n"],null,["# ee.Kernel.manhattan\n\nGenerates a distance kernel based on rectilinear (city-block) distance.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------------------------|---------|\n| `ee.Kernel.manhattan(radius, `*units* `, `*normalize* `, `*magnitude*`)` | Kernel |\n\n| Argument | Type | Details |\n|-------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `radius` | Float | The radius of the kernel to generate. |\n| `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. |\n| `normalize` | Boolean, default: false | Normalize the kernel values to sum to 1. |\n| `magnitude` | Float, default: 1 | Scale each value by this amount. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\nprint('A Manhattan kernel', ee.Kernel.manhattan({radius: 3}));\n\n/**\n * Output weights matrix\n *\n * [6, 5, 4, 3, 4, 5, 6]\n * [5, 4, 3, 2, 3, 4, 5]\n * [4, 3, 2, 1, 2, 3, 4]\n * [3, 2, 1, 0, 1, 2, 3]\n * [4, 3, 2, 1, 2, 3, 4]\n * [5, 4, 3, 2, 3, 4, 5]\n * [6, 5, 4, 3, 4, 5, 6]\n */\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\nfrom pprint import pprint\n\nprint('A Manhattan kernel:')\npprint(ee.Kernel.manhattan(**{'radius': 3}).getInfo())\n\n# Output weights matrix\n\n# [6, 5, 4, 3, 4, 5, 6]\n# [5, 4, 3, 2, 3, 4, 5]\n# [4, 3, 2, 1, 2, 3, 4]\n# [3, 2, 1, 0, 1, 2, 3]\n# [4, 3, 2, 1, 2, 3, 4]\n# [5, 4, 3, 2, 3, 4, 5]\n# [6, 5, 4, 3, 4, 5, 6]\n```"]]
