Mapping sanitation deprivations

Artificial intelligence, Javascript 2 comments

What household are deprived from adequate sanitation

Households in Cambodia have limited access to sanitation in some areas. In this exercise we use survey data to map what areas are most likely to be deprived.

step 1: copy the code below into the code editor. In this snippet we import a subset of the survey data and print the categories.

// import country data
var countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017");
var kh = countries.filter(ee.Filter.eq("country_na", "Cambodia"));

// import the reference data
// import the reference data
var referenceData = ee.FeatureCollection("projects/servir-mekong/undp/Training/toiletBinary")

Step 2: import the relevant datasets and combine them into a single image

// import relevant raster datasets
var planet  = ee.Image("projects/cemis-camp/assets/Planet/202012").select(["b1","b2","b3","b4"],["red","green","blue","nir"]);
var roadDistPrimary = ee.Image("projects/servir-mekong/staticMaps/primaryRoads").rename("primaryRoads");
var roadDistSecondary = ee.Image("projects/servir-mekong/staticMaps/secondaryRoads").rename("secondaryRoads");
var roadDistTertiary = ee.Image("projects/servir-mekong/undp/distanceLayers/tertiaryRoads").rename("tertiaryRoads");
var streamDist = ee.Image("WWF/HydroSHEDS/15ACC").rename("stream").unmask(0);
var nightlight = ee.ImageCollection("NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG").filterDate("2020-01-01","2020-12-31").select("avg_rad").mean();
var waterLines = ee.Image("projects/servir-mekong/undp/distanceLayers/waterLinesDistance").rename("water");
var well = ee.Image("projects/servir-mekong/undp/distanceLayers/wellDistance").rename("well");
var waterDist = ee.Image("projects/servir-mekong/undp/distance/waterDist").rename("waterDist");
var waterLines = ee.Image("projects/servir-mekong/undp/distance/waterlineDistance").unmask(0).rename("waterlines");
var wp2020 = ee.Image("WorldPop/GP/100m/pop/KHM_2020").rename("wp");
var forestDist = ee.Image("projects/servir-mekong/undp/distance/forestDist").rename("forestDist");


// combine raster data into a single image
var image = planet.addBands(roadDistPrimary )
                  .addBands(roadDistSecondary)
                  .addBands(roadDistTertiary)
                  .addBands(streamDist)
                  .addBands(nightlight)        
                  .addBands(well)
                  .addBands(waterDist)
                  .addBands(waterLines)
                  .addBands(wp2020)
                  .addBands(forestDist)

Step 3: store the band names in a variable, we need them later for the random forest algorithm.

var bandNames = image.bandNames();

Step 4: create a dataset for household with a connection to a water pipe and people without.

// create the training dataset by filtering
var myClass = referenceData.filter(ee.Filter.eq("hh_d_toile",0)).map(function(feat){return feat.set("class",1)}).limit(2000)
print(myClass.size())
var otherClass = referenceData.filter(ee.Filter.eq("hh_d_toile",1)).map(function(feat){return feat.set("class",0)}).limit(2000)
print(otherClass.size())

Step 6: combine the two datasets into a single dataset and split the data for training and validation

// merge the two classes
var trainingData = myClass.merge(otherClass)

Step 7: sample the image, this will store all the pixel values for the points

// sample the image
var trainingSample = image.sampleRegions({collection:training,scale:100});

Step 8: train the random forest classifier

// train the classifier in probability
var classifier = ee.Classifier.smileRandomForest(100).setOutputMode('PROBABILITY').train(trainingSample,"class",bandNames);

Step 9: get the variable importance and display it in a chart

// print the information of the classifier
var dict = classifier.explain();
print('Explain:',dict);

// get the variable importance
var variable_importance = ee.Feature(null, ee.Dictionary(dict).get('importance'));
 
// get the variable importance
var variables = ee.Dictionary(ee.Dictionary(dict).get('importance'));
var keys = variables.keys();

// create a chart of the variable importance and show the chart
var chart =
ui.Chart.feature.byProperty(variable_importance)
  .setChartType('ColumnChart')
  .setOptions({
  title: 'Random Forest Variable Importance',
  legend: {position: 'none'},
  hAxis: {title: 'Bands'},
  vAxis: {title: 'Importance'}
  });

// print the chart
print(chart);

Step 10: classify the image and display the map

// classify the image
var classification = image.classify(classifier);
 
 // add the layer to the map
Map.addLayer(classification.clip(kh),{min:0,max:1,palette:"darkred,red,orange,yellow,green,darkgreen"},"Machine Learning");

Find all the code combined in a single script here.

2 comments

  1. Greetings and Regards
    Thank you for your kindness
    It will be very good if your training is based on the introduction of shapefile to the Google Earth Engine system.
    to be usable everywhere.
    Thanks

    Like

    Reply

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