Mapping building materials

Artificial intelligence, Javascript Leave a comment

What construction materials are being used

Households in Cambodia have three main construction materials for their houses. Construction materials used are area dependent and in this exercise we will map the use of different materials in the country.

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
var referenceData = ee.FeatureCollection("projects/servir-mekong/undp/Training/housingMaterials");
// print categories
print(referenceData.aggregate_histogram("materials"))

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 item = "wood";
var myClass =  referenceData.filter(ee.Filter.eq("materials",item)).map(function(feat){return feat.set("class",1)});
var otherClass = referenceData.filter(ee.Filter.neq("materials",item)).map(function(feat){return feat.set("class",0)}) //.limit(1576);

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)

// add a random number to each column
var trainingData = trainingData.randomColumn("random")

var training = trainingData.filter(ee.Filter.lt("random",0.8))
var validation = trainingData.filter(ee.Filter.gte("random",0.8))

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"},"edu Machine Learning");

Step 11: Display all the categories

// filter for different water distribution types
var wood = referenceData.filter(ee.Filter.eq("materials","wood"));
var concrete = referenceData.filter(ee.Filter.eq("materials","concrete"));
var iron = referenceData.filter(ee.Filter.eq("materials","iron"));

Map.addLayer(wood,{},"wood");
Map.addLayer(concrete.draw("red"),{},"concrete");
Map.addLayer(iron.draw("blue"),{},"iron");

Step 12: use the validation data to analyse the results

print("histogram for class");
print(ui.Chart.image.histogram(classification,validation.filter(ee.Filter.eq("class",1)),100).setOptions({hAxis: {title: 'probability', maxValue: 1, minValue: 0}}));
print("histogram for other");
print(ui.Chart.image.histogram(classification,validation.filter(ee.Filter.eq("class",0)),100).setOptions({hAxis: {title: 'probability', maxValue: 1, minValue: 0}}));

Find all the code combined in a single script here.

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