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ee.Image.distance
コレクションでコンテンツを整理
必要に応じて、コンテンツの保存と分類を行います。
指定された距離カーネルを使用して、各バンド内のゼロ以外の最も近いピクセルまでの距離を計算します。
用途 戻り値 Image. distance (kernel , skipMasked )
画像
引数 タイプ 詳細 これ: image
画像 入力画像。 kernel
カーネル、デフォルト: null 距離カーネル。chebyshev、euclidean、manhattan のいずれか。 skipMasked
ブール値。デフォルト値は true です。 対応する入力ピクセルがマスクされている場合は、出力ピクセルをマスクします。
例
コードエディタ(JavaScript)
// The objective is to determine the per-pixel distance to a target
// feature (pixel value). In this example, the target feature is water in a
// land cover map.
// Import a Dynamic World land cover image and subset the 'label' band.
var lcImg = ee . Image (
'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS' )
. select ( 'label' );
// Create a binary image where the target feature is value 1, all else 0.
// In the Dynamic World map, water is represented as value 0, so we use the
// ee.Image.eq() relational operator to set it to 1.
var targetImg = lcImg . eq ( 0 );
// Set a max distance from target pixels to consider in the analysis. Pixels
// with distance greater than this value from target pixels will be masked out.
// Here, we are using units of meters, but the distance kernels also accept
// units of pixels.
var maxDistM = 10000 ; // 10 km
// Calculate distance to target pixels. Several distance kernels are provided.
// Euclidean distance.
var euclideanKernel = ee . Kernel . euclidean ( maxDistM , 'meters' );
var euclideanDist = targetImg . distance ( euclideanKernel );
var vis = { min : 0 , max : maxDistM };
Map . setCenter ( - 95.68 , 46.46 , 9 );
Map . addLayer ( euclideanDist , vis , 'Euclidean distance to target pixels' );
// Manhattan distance.
var manhattanKernel = ee . Kernel . manhattan ( maxDistM , 'meters' );
var manhattanDist = targetImg . distance ( manhattanKernel );
Map . addLayer ( manhattanDist , vis , 'Manhattan distance to target pixels' , false );
// Chebyshev distance.
var chebyshevKernel = ee . Kernel . chebyshev ( maxDistM , 'meters' );
var chebyshevDist = targetImg . distance ( chebyshevKernel );
Map . addLayer ( chebyshevDist , vis , 'Chebyshev distance to target pixels' , false );
// Add the target layer to the map; water is blue, all else masked out.
Map . addLayer ( targetImg . mask ( targetImg ), { palette : 'blue' }, 'Target pixels' );
Python の設定
Python API とインタラクティブな開発での geemap
の使用については、
Python 環境 のページをご覧ください。
import ee
import geemap.core as geemap
Colab(Python)
# The objective is to determine the per-pixel distance to a target
# feature (pixel value). In this example, the target feature is water in a
# land cover map.
# Import a Dynamic World land cover image and subset the 'label' band.
lc_img = ee . Image (
'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS'
) . select ( 'label' )
# Create a binary image where the target feature is value 1, all else 0.
# In the Dynamic World map, water is represented as value 0, so we use the
# ee.Image.eq() relational operator to set it to 1.
target_img = lc_img . eq ( 0 )
# Set a max distance from target pixels to consider in the analysis. Pixels
# with distance greater than this value from target pixels will be masked out.
# Here, we are using units of meters, but the distance kernels also accept
# units of pixels.
max_dist_m = 10000 # 10 km
# Calculate distance to target pixels. Several distance kernels are provided.
# Euclidean distance.
euclidean_kernel = ee . Kernel . euclidean ( max_dist_m , 'meters' )
euclidean_dist = target_img . distance ( euclidean_kernel )
vis = { 'min' : 0 , 'max' : max_dist_m }
m = geemap . Map ()
m . set_center ( - 95.68 , 46.46 , 9 )
m . add_layer ( euclidean_dist , vis , 'Euclidean distance to target pixels' )
# Manhattan distance.
manhattan_kernel = ee . Kernel . manhattan ( max_dist_m , 'meters' )
manhattan_dist = target_img . distance ( manhattan_kernel )
m . add_layer (
manhattan_dist , vis , 'Manhattan distance to target pixels' , False
)
# Chebyshev distance.
chebyshev_kernel = ee . Kernel . chebyshev ( max_dist_m , 'meters' )
chebyshev_dist = target_img . distance ( chebyshev_kernel )
m . add_layer (
chebyshev_dist , vis , 'Chebyshev distance to target pixels' , False
)
# Add the target layer to the map water is blue, all else masked out.
m . add_layer (
target_img . mask ( target_img ), { 'palette' : 'blue' }, 'Target pixels'
)
m
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特に記載のない限り、このページのコンテンツはクリエイティブ・コモンズの表示 4.0 ライセンス により使用許諾されます。コードサンプルは Apache 2.0 ライセンス により使用許諾されます。詳しくは、Google Developers サイトのポリシー をご覧ください。Java は Oracle および関連会社の登録商標です。
最終更新日 2025-07-26 UTC。
ご意見をお聞かせください
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["必要な情報がない","missingTheInformationINeed","thumb-down"],["複雑すぎる / 手順が多すぎる","tooComplicatedTooManySteps","thumb-down"],["最新ではない","outOfDate","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["サンプル / コードに問題がある","samplesCodeIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-07-26 UTC。"],[[["Computes the distance to the nearest non-zero pixel for each band in an image, using a specified distance kernel (Chebyshev, Euclidean, or Manhattan)."],["Accepts an input image, a distance kernel, and an optional parameter to mask output pixels corresponding to masked input pixels."],["Returns an image where pixel values represent the distance to the nearest non-zero pixel in the input."],["Offers flexibility in defining the distance kernel and handling masked pixels."],["Can be used to analyze proximity to specific features in images, such as determining the distance to water bodies in a land cover map."]]],[]]