FeatureCollection
की प्रॉपर्टी में डेटा इकट्ठा करने के लिए, featureCollection.reduceColumns()
का इस्तेमाल करें. उदाहरण के लिए, वॉटरशेड FeatureCollection
में एरिया प्रॉपर्टी की जांच करने के लिए, यह कोड Earth Engine के हिसाब से एरिया के हिसाब से रूट मीन स्क्वेयर
गड़बड़ी (आरएमएसई) का हिसाब लगाता है:
// Load watersheds from a data table and filter to the continental US. var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06') .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)); // This function computes the squared difference between an area property // and area computed directly from the feature's geometry. var areaDiff = function(feature) { // Compute area in sq. km directly from the geometry. var area = feature.geometry().area().divide(1000 * 1000); // Compute the difference between computed area and the area property. var diff = area.subtract(ee.Number.parse(feature.get('areasqkm'))); // Return the feature with the squared difference set to the 'diff' property. return feature.set('diff', diff.pow(2)); }; // Calculate RMSE for population of difference pairs. var rmse = ee.Number( // Map the difference function over the collection. sheds.map(areaDiff) // Reduce to get the mean squared difference. .reduceColumns(ee.Reducer.mean(), ['diff']) .get('mean') ) // Compute the square root of the mean square to get RMSE. .sqrt(); // Print the result. print('RMSE=', rmse);
import ee import geemap.core as geemap
# Load watersheds from a data table and filter to the continental US. sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds( ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29) ) # This function computes the squared difference between an area property # and area computed directly from the feature's geometry. def area_diff(feature): # Compute area in sq. km directly from the geometry. area = feature.geometry().area().divide(1000 * 1000) # Compute the difference between computed area and the area property. diff = area.subtract(ee.Number.parse(feature.get('areasqkm'))) # Return the feature with the squared difference set to the 'diff' property. return feature.set('diff', diff.pow(2)) # Calculate RMSE for population of difference pairs. rmse = ( ee.Number( # Map the difference function over the collection. sheds.map(area_diff) # Reduce to get the mean squared difference. .reduceColumns(ee.Reducer.mean(), ['diff']).get('mean') ) # Compute the square root of the mean square to get RMSE. .sqrt() ) # Print the result. display('RMSE=', rmse)
इस उदाहरण में, ध्यान दें कि reduceColumns()
की रिटर्न वैल्यू, ‘mean’
कीवर्ड वाली एक डिक्शनरी है. औसत निकालने के लिए, dictionary.get()
के नतीजे को ee.Number()
वाले नंबर में बदलें. इसके बाद, उस पर sqrt()
को कॉल करें. Earth Engine में सहायक
डेटा स्ट्रक्चर के बारे में ज़्यादा जानने के लिए, यह
ट्यूटोरियल देखें.
इमेज पर सुविधाओं को ओवरले करने के लिए, featureCollection.reduceRegions()
का इस्तेमाल करें. उदाहरण के लिए, अमेरिका के कॉन्टिनेंटल वाटरशेड में बारिश की मात्रा का हिसाब लगाने के लिए, reduceRegions()
के बाद map()
का इस्तेमाल करें:
// Load an image of daily precipitation in mm/day. var precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first()); // Load watersheds from a data table and filter to the continental US. var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06') .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)); // Add the mean of each image as new properties of each feature. var withPrecip = precip.reduceRegions(sheds, ee.Reducer.mean()) .filter(ee.Filter.notNull(['prcp'])); // This function computes total rainfall in cubic meters. var prcpVolume = function(feature) { // Precipitation in mm/day -> meters -> sq. meters. var volume = ee.Number(feature.get('prcp')) .divide(1000).multiply(feature.geometry().area()); return feature.set('volume', volume); }; var highVolume = withPrecip // Map the function over the collection. .map(prcpVolume) // Sort descending. .sort('volume', false) // Get only the 5 highest volume watersheds. .limit(5) // Extract the names to a list. .reduceColumns(ee.Reducer.toList(), ['name']).get('list'); // Print the resulting FeatureCollection. print(highVolume);
import ee import geemap.core as geemap
# Load an image of daily precipitation in mm/day. precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first()) # Load watersheds from a data table and filter to the continental US. sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds( ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29) ) # Add the mean of each image as new properties of each feature. with_precip = precip.reduceRegions(sheds, ee.Reducer.mean()).filter( ee.Filter.notNull(['prcp']) ) # This function computes total rainfall in cubic meters. def prcp_volume(feature): # Precipitation in mm/day -> meters -> sq. meters. volume = ( ee.Number(feature.get('prcp')) .divide(1000) .multiply(feature.geometry().area()) ) return feature.set('volume', volume) high_volume = ( # Map the function over the collection. with_precip.map(prcp_volume) # Sort descending and get only the 5 highest volume watersheds. .sort('volume', False).limit(5) # Extract the names to a list. .reduceColumns(ee.Reducer.toList(), ['name']).get('list') ) # Print the resulting FeatureCollection. display(high_volume)
फ़ीचर कलेक्शन को कम करने के बारे में ज़्यादा जानने के लिए, FeatureCollection कॉलम के आंकड़े और वेक्टर से रेस्टर में बदलाव देखें.