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I am working on applying the Change Detection using the CCDC algorithm for individual polygons in Google Earth Engine. My script functions correctly, but it runs quite slowly. I would like to know how I can increase its processing speed. Below is my current script:

// Define the coordinates for the MultiPolygon
var coordinates = [
  [
    [
      [176.633277937763, -38.52948922939942],
      [176.64701084791926, -38.52948922939942],
      [176.64701084791926, -38.517670864333496],
      [176.633277937763, -38.517670864333496],
      [176.633277937763, -38.52948922939942]  
    ]
  ],
  [
    [
      [176.51517491041926, -38.545602053961694],
      [176.53028111159114, -38.545602053961694],
      [176.53028111159114, -38.534860571938644],
      [176.51517491041926, -38.534860571938644],
      [176.51517491041926, -38.545602053961694]  
    ]
  ]
];

// Create the MultiPolygon geometry
var studyRegion = ee.Geometry.MultiPolygon(coordinates, null, false);
Map.centerObject(studyRegion);
Map.addLayer(studyRegion, {color: 'red'}, 'MultiPolygon');

// Sentinel-1 filtering
function get_s1_col(studyRegion, start_date, end_date) {
    return ee.ImageCollection('COPERNICUS/S1_GRD')
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
        .filterDate(start_date, end_date)
        .filterBounds(studyRegion);
}

// Calculate VV/VH ratio
var calculateVVVHRatio = function(image) {
    var vv = image.select('VV').toFloat();
    var vh = image.select('VH').toFloat();
    var ratio = vh.subtract(vv).divide(vv.add(vh)).rename('VH_VV_ratio');
    return ee.Image.cat([vv, vh, ratio]).rename(['VV', 'VH', 'ratioVH_VV']);
};

// Clip each image in the collection
var clipCollection = function(image) {
    return image.clip(studyRegion);
};

// Process Sentinel-1 collection
var process_s1_collection = function(studyRegion, start_date, end_date) {
    return get_s1_col(studyRegion, start_date, end_date)
        .map(clipCollection)
        .map(calculateVVVHRatio);
};

// Process each polygon in the feature collection
var processPolygon = function(feature) {
    var polygon = feature.geometry();
    var s1_collection = process_s1_collection(polygon, '2021-01-01', '2024-02-04');

    var ccdParams = {
        breakpointBands: ['VH', 'VV', 'ratioVH_VV'],
        minObservations: 4,
        chiSquareProbability: 0.85,
        minNumOfYearsScaler: 0.5,
        dateFormat: 1,
        lambda: 2,
        maxIterations: 10000,
        collection: s1_collection
    };

    var ccdResults = ee.Algorithms.TemporalSegmentation.Ccdc(ccdParams);
    var change = ccdResults.select('tBreak');
    var changeProb = ccdResults.select('changeProb');

    var mask = change.gt(2024.0).and(change.lte(2024.0956284153006)).and(changeProb.eq(1));
    var magnitudes = ccdResults.select('ratioVH_VV_magnitude').arrayMask(mask);
    
    var negativeMagnitudes = magnitudes.arrayMask(magnitudes.lt(0));
    var maxAbsNegativeIndex = negativeMagnitudes.abs().arrayArgmax().arrayFlatten([['index']]);
    
    return negativeMagnitudes.arrayGet(maxAbsNegativeIndex).rename('maxNegativeMagnitude');
};

// Create an empty image collection to store results
var resultsCollection = ee.ImageCollection([]);
// Map over the feature collection and process each polygon
var processFeatureCollection = function(fc) {
  var results = fc.map(function(feature) {
    return ee.Image(processPolygon(feature)).set('polygon_id', feature.id());
  });
  
  return results;
};

// Define the feature collection
var featureCollection = ee.FeatureCollection(studyRegion);

// Process the feature collection
var processedResults = processFeatureCollection(featureCollection);
print(processedResults)

// Merge the results into a single image
var mergedResults = ee.ImageCollection(processedResults).mosaic();

// Export the merged result to Google Drive
Export.image.toDrive({
  image: mergedResults,
  description: 'Merged_CCDC_Results',
  scale: 10,
  maxPixels: 3784216672400,
  region: studyRegion
});

The link of my script here: https://code.earthengine.google.com/fdff2f56230da7825c1e03b46183b1a5

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1 Answer 1

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1

I ran your code and the task completed in 4 minutes. I tried these changes and the task completed in 5 minutes.

Following!

Final Optimized Workflow

  1. Convert MultiPolygon to FeatureCollection manually.
  2. Clip images before mapping calculations.
  3. Avoid mapping functions when only handling 2 polygons.
  4. Run CCDC on a composite image rather than a full collection.
  5. Reduce maxPixels in Export.image.toDrive().
// Convert MultiPolygon to FeatureCollection
var featureCollection = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Polygon(coordinates[0][0])),
  ee.Feature(ee.Geometry.Polygon(coordinates[1][0]))
]);

// Function to process Sentinel-1 collection with efficient clipping
var process_s1_collection = function(studyRegion, start_date, end_date) {
    var clippedCollection = get_s1_col(studyRegion, start_date, end_date).map(clipCollection);
    return clippedCollection.map(calculateVVVHRatio);
};

// Process each polygon separately (Avoid unnecessary mapping)
var result1 = processPolygon(featureCollection.toList(2).get(0));
var result2 = processPolygon(featureCollection.toList(2).get(1));

var processedResults = ee.ImageCollection([result1, result2]);

// Merge results into one image
var mergedResults = processedResults.mosaic();

// Export with optimized maxPixels
Export.image.toDrive({
  image: mergedResults,
  description: 'Merged_CCDC_Results',
  scale: 10,
  maxPixels: 1e9,
  region: studyRegion
});
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