Google Earth Engine Python – Adding a New Column to FeatureCollection in GEE

feature-collectiongoogle-earth-enginepointpython

I want to add random values to a new property column landcover in the feature collection, but it only yields one value for all feature/points. It should be random values for each feature. Here is my code

import ee
import random
points=ee.FeatureCollection("users/miketu72/2021_LD_test_point")
# Add a column named landcover with random values from 1 to 4
def landcover(feat):
    code=list("1234")
    num=random.choice(code)
    newfeat=feat.set("landcover",ee.Number(int(num)))
    return newfeat
newPoints=points.map(landcover)
newPoints.getInfo()

Best Answer

Like Noel said, use randomColumn():

# Create some features for testing
points = ee.FeatureCollection([
    ee.Feature(None, {'i': i})
    for i in range(0, 10)
])

new_points = points \
    .randomColumn('landcover') \
    # Turn landcover from a float between 0 and 1 to int between 1 and 4
    .map(lambda feature: 
         feature.set(
             'landcover', 
             feature.getNumber('landcover').multiply(4).ceil()
         )
    )

new_points.getInfo()

Related Solutions

[GIS] Google Earth Engine, Supervised Classification

I'd do something like this:

// Landsat 5 TM Raw Image Collection
var lt5 = ee.ImageCollection("LANDSAT/LT5_L1T");

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Early = lt5.filterDate('1985-01-01', '1986-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud = ee.Image(lakeTiticaca_Early.sort('CLOUD_COVER').first());

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Later = lt5.filterDate('2011-01-01', '2012-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud2 = ee.Image(lakeTiticaca_Later.sort('CLOUD_COVER').first());

// Applying MNDWI Function to Least Cloudy Images 
var mNDWI_Early = leastCloud.expression(
  '(Green - MIR) / (Green + MIR)', {
  'Green': leastCloud.select('B2'),
  'MIR': leastCloud.select('B5')
 });

var mNDWI_Later = leastCloud2.expression(
 '(Green - MIR) / (Green + MIR)', {
 'Green': leastCloud2.select('B2'),
 'MIR': leastCloud2.select('B5')
 });

// Creating an image collection from the MNDWI calculated Images 
var constant1 = ee.Image(mNDWI_Early).select([0],["mNDWI"]);
Map.addLayer(leastCloud,{bands:["B4","B5","B3"]},"before")
var constant2 = ee.Image(mNDWI_Later).select([0],["mNDWI"]);
Map.addLayer(leastCloud2,{bands:["B4","B5","B3"]},"after")

var dif = constant2.subtract(constant1)
Map.addLayer(dif,{},"dif")

var img1 = leastCloud.addBands(constant1)
var img2 = leastCloud2.addBands(constant2)

var final = ee.Image.cat(img1,img2)
Map.addLayer(final,{},"final")

//* Supervised Image Classification *//

var training = ee.FeatureCollection([water_nochange, water_gain, water_loss, land_nochange]).flatten()

var sample = final.sampleRegions(training, ["class"], 30)

var trained = ee.Classifier.cart().train(sample, "class")

var classified = final.classify(trained)
Map.addLayer(classified,{min:1, max:4, palette:["#b3b3ff","#0000e6", "#ff0000", "#009900"]},"classified")

You can access the feature collections in the following link: https://code.earthengine.google.com/3f4c2908fcf7c469357b80783f3ef26f

You should mask clouds, use another path/row to include the missing part of the lake, and make more training points. You can also try another classifier like svm, randomForest, etc.

Google Earth Engine stratifiedSample – Random Point in Each Image in Google Earth Engine

The randomness in stratifiedSample is based on the pixel's location and the seed. Your best bet is to use some portion of the image's metadata as the seed. If there's nothing there to use (like a start time), then assign a random number to each image using randomColumn and use that (multiplied by some large constant):

var sampler = function(image){
  var seed = image.getNumber('random').multiply(1000000).int()
  var sample = image.stratifiedSample({
      seed: seed
      ...
   })
}
var mySamples = ic.randomColumn().map(sampler)

Best Answer

Related Solutions

[GIS] Google Earth Engine, Supervised Classification

Google Earth Engine stratifiedSample – Random Point in Each Image in Google Earth Engine

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