ImageCollection के बारे में खास जानकारी
संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
ImageCollection, इमेज का स्टैक या क्रम होता है.
कलेक्शन आईडी से बनाना
ImageCollection को लोड करने के लिए, Earth Engine ऐसेट आईडी को ImageCollection कंस्ट्रक्टर में चिपकाएं. डेटा कैटलॉग में,
ImageCollection आईडी देखे जा सकते हैं. उदाहरण के लिए, Sentinel-2 के ज़रिए इकट्ठा किए गए, सतह के रिफ़्लेक्शन डेटा का कलेक्शन लोड करने के लिए:
कोड एडिटर (JavaScript)
var sentinelCollection = ee.ImageCollection('COPERNICUS/S2_SR');
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
import ee
import geemap.core as geemap
Colab (Python)
sentinel_collection = ee.ImageCollection('COPERNICUS/S2_SR')
इस कलेक्शन में, सार्वजनिक कैटलॉग में मौजूद हर Sentinel-2 इमेज शामिल होती है.
बहुत सारे हैं. आम तौर पर, संग्रह को यहां या
यहां दिखाए गए तरीके से फ़िल्टर किया जाता है.
इमेज की सूची से बनाना
कॉन्स्ट्रक्टर
ee.ImageCollection() या सुविधाजनक तरीका
ee.ImageCollection.fromImages(), इमेज की सूचियों से इमेज कलेक्शन बनाता है. मौजूदा कलेक्शन को मर्ज करके भी, इमेज के नए कलेक्शन बनाए जा सकते हैं. उदाहरण के लिए:
कोड एडिटर (JavaScript)
// Create arbitrary constant images.
var constant1 = ee.Image(1);
var constant2 = ee.Image(2);
// Create a collection by giving a list to the constructor.
var collectionFromConstructor = ee.ImageCollection([constant1, constant2]);
print('collectionFromConstructor: ', collectionFromConstructor);
// Create a collection with fromImages().
var collectionFromImages = ee.ImageCollection.fromImages(
[ee.Image(3), ee.Image(4)]);
print('collectionFromImages: ', collectionFromImages);
// Merge two collections.
var mergedCollection = collectionFromConstructor.merge(collectionFromImages);
print('mergedCollection: ', mergedCollection);
// Create a toy FeatureCollection
var features = ee.FeatureCollection(
[ee.Feature(null, {foo: 1}), ee.Feature(null, {foo: 2})]);
// Create an ImageCollection from the FeatureCollection
// by mapping a function over the FeatureCollection.
var images = features.map(function(feature) {
return ee.Image(ee.Number(feature.get('foo')));
});
// Print the resultant collection.
print('Image collection: ', images);
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
import ee
import geemap.core as geemap
Colab (Python)
# Create arbitrary constant images.
constant_1 = ee.Image(1)
constant_2 = ee.Image(2)
# Create a collection by giving a list to the constructor.
collection_from_constructor = ee.ImageCollection([constant_1, constant_2])
display('Collection from constructor:', collection_from_constructor)
# Create a collection with fromImages().
collection_from_images = ee.ImageCollection.fromImages(
[ee.Image(3), ee.Image(4)]
)
display('Collection from images:', collection_from_images)
# Merge two collections.
merged_collection = collection_from_constructor.merge(collection_from_images)
display('Merged collection:', merged_collection)
# Create a toy FeatureCollection
features = ee.FeatureCollection(
[ee.Feature(None, {'foo': 1}), ee.Feature(None, {'foo': 2})]
)
# Create an ImageCollection from the FeatureCollection
# by mapping a function over the FeatureCollection.
images = features.map(lambda feature: ee.Image(ee.Number(feature.get('foo'))))
# Display the resultant collection.
display('Image collection:', images)
ध्यान दें कि इस उदाहरण में, ImageCollection बनाने के लिए, FeatureCollection पर Image दिखाने वाले फ़ंक्शन को मैप किया गया है. ImageCollection सेक्शन में मैपिंग में मैपिंग के बारे में ज़्यादा जानें. FeatureCollection सेक्शन में जाकर, सुविधा कलेक्शन के बारे में ज़्यादा जानें.
सीओजी की सूची से बनाना
Cloud Storage में मौजूद GeoTiff फ़ाइलों से ImageCollection बनाएं.
उदाहरण के लिए:
कोड एडिटर (JavaScript)
// All the GeoTiffs are in this folder.
var uriBase = 'gs://gcp-public-data-landsat/LC08/01/001/002/' +
'LC08_L1GT_001002_20160817_20170322_01_T2/';
// List of URIs, one for each band.
var uris = ee.List([
uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',
uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',
uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',
uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',
]);
// Make a collection from the list of images.
var images = uris.map(ee.Image.loadGeoTIFF);
var collection = ee.ImageCollection(images);
// Get an RGB image from the collection of bands.
var rgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5']);
Map.centerObject(rgb);
Map.addLayer(rgb, {bands: ['B4', 'B3', 'B2'], min: 0, max: 20000}, 'rgb');
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
import ee
import geemap.core as geemap
Colab (Python)
# All the GeoTiffs are in this folder.
uri_base = (
'gs://gcp-public-data-landsat/LC08/01/001/002/'
+ 'LC08_L1GT_001002_20160817_20170322_01_T2/'
)
# List of URIs, one for each band.
uris = ee.List([
uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',
uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',
uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',
uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',
])
# Make a collection from the list of images.
images = uris.map(lambda uri: ee.Image.loadGeoTIFF(uri))
collection = ee.ImageCollection(images)
# Get an RGB image from the collection of bands.
rgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5'])
m = geemap.Map()
m.center_object(rgb)
m.add_layer(rgb, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 20000}, 'rgb')
m
Cloud GeoTiffs से इमेज लोड करने के बारे में ज़्यादा जानें.
Zarr v2 कलेक्शन से बनाना
ज़्यादा डाइमेंशन में इमेज स्लाइस लेकर, Cloud Storage में Zarr v2 कलेक्शन से ImageCollection बनाएं.
उदाहरण के लिए:
कोड एडिटर (JavaScript)
var timeStart = 1000000;
var timeEnd = 1000048;
var zarrV2ArrayImages = ee.ImageCollection.loadZarrV2Array({
uri:
'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
proj: 'EPSG:4326',
axis: 0,
starts: [timeStart],
ends: [timeEnd]
});
print(zarrV2ArrayImages);
Map.addLayer(zarrV2ArrayImages, {min: -0.0001, max: 0.00005}, 'Evaporation');
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
import ee
import geemap.core as geemap
Colab (Python)
time_start = 1000000
time_end = 1000048
zarr_v2_array_images = ee.ImageCollection.loadZarrV2Array(
uri='gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',
proj='EPSG:4326',
axis=0,
starts=[time_start],
ends=[time_end],
)
display(zarr_v2_array_images)
m = geemap.Map()
m.add_layer(
zarr_v2_array_images, {'min': -0.0001, 'max': 0.00005}, 'Evaporation'
)
m
जब तक कुछ अलग से न बताया जाए, तब तक इस पेज की सामग्री को Creative Commons Attribution 4.0 License के तहत और कोड के नमूनों को Apache 2.0 License के तहत लाइसेंस मिला है. ज़्यादा जानकारी के लिए, Google Developers साइट नीतियां देखें. Oracle और/या इससे जुड़ी हुई कंपनियों का, Java एक रजिस्टर किया हुआ ट्रेडमार्क है.
आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया.
[null,null,["आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया."],[[["\u003cp\u003eAn \u003ccode\u003eImageCollection\u003c/code\u003e in Earth Engine represents a sequence of images and can be loaded using an Earth Engine asset ID from the data catalog.\u003c/p\u003e\n"],["\u003cp\u003e\u003ccode\u003eImageCollection\u003c/code\u003es can be created using various methods, including \u003ccode\u003eee.ImageCollection()\u003c/code\u003e, \u003ccode\u003eee.ImageCollection.fromImages()\u003c/code\u003e, merging existing collections, or by mapping a function over a \u003ccode\u003eFeatureCollection\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eUsers often filter large \u003ccode\u003eImageCollection\u003c/code\u003es, such as the Sentinel-2 surface reflectance collection, to focus on specific images of interest.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine allows creating \u003ccode\u003eImageCollection\u003c/code\u003es from GeoTIFFs stored in Cloud Storage by mapping \u003ccode\u003eee.Image.loadGeoTIFF\u003c/code\u003e over a list of URIs.\u003c/p\u003e\n"]]],["`ImageCollection` can be loaded using Earth Engine asset IDs, like 'COPERNICUS/S2_SR'. Collections can be created using `ee.ImageCollection()` or `ee.ImageCollection.fromImages()`, which take lists of images. Existing collections can be merged with the `merge()` method. `ImageCollection`s are also created by mapping a function over a `FeatureCollection` that returns an `Image`. Images can also be imported from GeoTIFF files in Cloud Storage, mapped and then put into an `ImageCollection`.\n"],null,["# ImageCollection Overview\n\nAn `ImageCollection` is a stack or sequence of images.\n\nConstruct from a collection ID\n------------------------------\n\nAn `ImageCollection` can be loaded by pasting an Earth Engine\nasset ID into the\n`ImageCollection` constructor. You can find\n`ImageCollection` IDs in the [data catalog](/earth-engine/datasets). For example, to load the\n[Sentinel-2 surface reflectance\ncollection](/earth-engine/guides/datasets/catalog/COPERNICUS_S2_SR):\n\n### Code Editor (JavaScript)\n\n```javascript\nvar sentinelCollection = ee.ImageCollection('COPERNICUS/S2_SR');\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\nsentinel_collection = ee.ImageCollection('COPERNICUS/S2_SR')\n```\n\nThis collection contains every Sentinel-2 image in the public catalog.\nThere are a lot. Usually you want to filter the collection as shown [here](/earth-engine/guides/ic_info) or\n[here](/earth-engine/guides/ic_filtering).\n\nConstruct from an image list\n----------------------------\n\nThe constructor\n`ee.ImageCollection()` or the convenience method\n`ee.ImageCollection.fromImages()` create image collections from\nlists of images. You can also create new image collections by merging\nexisting collections. For example:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Create arbitrary constant images.\nvar constant1 = ee.Image(1);\nvar constant2 = ee.Image(2);\n\n// Create a collection by giving a list to the constructor.\nvar collectionFromConstructor = ee.ImageCollection([constant1, constant2]);\nprint('collectionFromConstructor: ', collectionFromConstructor);\n\n// Create a collection with fromImages().\nvar collectionFromImages = ee.ImageCollection.fromImages(\n [ee.Image(3), ee.Image(4)]);\nprint('collectionFromImages: ', collectionFromImages);\n\n// Merge two collections.\nvar mergedCollection = collectionFromConstructor.merge(collectionFromImages);\nprint('mergedCollection: ', mergedCollection);\n\n// Create a toy FeatureCollection\nvar features = ee.FeatureCollection(\n [ee.Feature(null, {foo: 1}), ee.Feature(null, {foo: 2})]);\n\n// Create an ImageCollection from the FeatureCollection\n// by mapping a function over the FeatureCollection.\nvar images = features.map(function(feature) {\n return ee.Image(ee.Number(feature.get('foo')));\n});\n\n// Print the resultant collection.\nprint('Image collection: ', images);\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\n# Create arbitrary constant images.\nconstant_1 = ee.Image(1)\nconstant_2 = ee.Image(2)\n\n# Create a collection by giving a list to the constructor.\ncollection_from_constructor = ee.ImageCollection([constant_1, constant_2])\ndisplay('Collection from constructor:', collection_from_constructor)\n\n# Create a collection with fromImages().\ncollection_from_images = ee.ImageCollection.fromImages(\n [ee.Image(3), ee.Image(4)]\n)\ndisplay('Collection from images:', collection_from_images)\n\n# Merge two collections.\nmerged_collection = collection_from_constructor.merge(collection_from_images)\ndisplay('Merged collection:', merged_collection)\n\n# Create a toy FeatureCollection\nfeatures = ee.FeatureCollection(\n [ee.Feature(None, {'foo': 1}), ee.Feature(None, {'foo': 2})]\n)\n\n# Create an ImageCollection from the FeatureCollection\n# by mapping a function over the FeatureCollection.\nimages = features.map(lambda feature: ee.Image(ee.Number(feature.get('foo'))))\n\n# Display the resultant collection.\ndisplay('Image collection:', images)\n```\n\nNote that in this example an `ImageCollection` is created by\nmapping a function that returns an `Image` over a\n`FeatureCollection`. Learn more about mapping in the [Mapping over an ImageCollection section](/earth-engine/guides/ic_mapping). Learn\nmore about feature collections from the\n[FeatureCollection section](/earth-engine/guides/feature_collections).\n\nConstruct from a COG list\n-------------------------\n\nCreate an `ImageCollection` from GeoTiffs in Cloud Storage.\nFor example:\n\n### Code Editor (JavaScript)\n\n```javascript\n// All the GeoTiffs are in this folder.\nvar uriBase = 'gs://gcp-public-data-landsat/LC08/01/001/002/' +\n 'LC08_L1GT_001002_20160817_20170322_01_T2/';\n\n// List of URIs, one for each band.\nvar uris = ee.List([\n uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',\n uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',\n uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',\n uriBase + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',\n]);\n\n// Make a collection from the list of images.\nvar images = uris.map(ee.Image.loadGeoTIFF);\nvar collection = ee.ImageCollection(images);\n\n// Get an RGB image from the collection of bands.\nvar rgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5']);\nMap.centerObject(rgb);\nMap.addLayer(rgb, {bands: ['B4', 'B3', 'B2'], min: 0, max: 20000}, 'rgb');\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\n# All the GeoTiffs are in this folder.\nuri_base = (\n 'gs://gcp-public-data-landsat/LC08/01/001/002/'\n + 'LC08_L1GT_001002_20160817_20170322_01_T2/'\n)\n\n# List of URIs, one for each band.\nuris = ee.List([\n uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B2.TIF',\n uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B3.TIF',\n uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B4.TIF',\n uri_base + 'LC08_L1GT_001002_20160817_20170322_01_T2_B5.TIF',\n])\n\n# Make a collection from the list of images.\nimages = uris.map(lambda uri: ee.Image.loadGeoTIFF(uri))\ncollection = ee.ImageCollection(images)\n\n# Get an RGB image from the collection of bands.\nrgb = collection.toBands().rename(['B2', 'B3', 'B4', 'B5'])\nm = geemap.Map()\nm.center_object(rgb)\nm.add_layer(rgb, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 20000}, 'rgb')\nm\n```\n\n[Learn more about\nloading images from Cloud GeoTiffs](/earth-engine/guides/image_overview#images-from-cloud-geotiffs).\n\nConstruct from a Zarr v2 array\n------------------------------\n\nCreate an `ImageCollection` from a Zarr\nv2 array in Cloud Storage by taking image slices along a higher dimension.\nFor example:\n\n### Code Editor (JavaScript)\n\n```javascript\nvar timeStart = 1000000;\nvar timeEnd = 1000048;\nvar zarrV2ArrayImages = ee.ImageCollection.loadZarrV2Array({\n uri:\n 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',\n proj: 'EPSG:4326',\n axis: 0,\n starts: [timeStart],\n ends: [timeEnd]\n});\n\nprint(zarrV2ArrayImages);\n\nMap.addLayer(zarrV2ArrayImages, {min: -0.0001, max: 0.00005}, 'Evaporation');\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\ntime_start = 1000000\ntime_end = 1000048\nzarr_v2_array_images = ee.ImageCollection.loadZarrV2Array(\n uri='gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3/evaporation/.zarray',\n proj='EPSG:4326',\n axis=0,\n starts=[time_start],\n ends=[time_end],\n)\n\ndisplay(zarr_v2_array_images)\n\nm = geemap.Map()\nm.add_layer(\n zarr_v2_array_images, {'min': -0.0001, 'max': 0.00005}, 'Evaporation'\n)\nm\n```"]]
