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Updating randomForest to smileRandomForest #4

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Updating randomForest to smileRandomForest #4

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19 changes: 13 additions & 6 deletions _episodes/05-classify-imagery.md
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Expand Up @@ -27,7 +27,7 @@ Google Earth Engine provides users with the opportunity to conduct many advanced
The purpose is to get a classified map of land cover in an area of interest. We will examine Landsat imagery and manually identify a set of training points for three classes (water, forest, urban). We will then use those training points to train a classifier. The classifier will be used to classify the rest of the Landsat image into those three categories. We can then assess the accuracy of our classification using `classifier.confusionMatrix()`.

Link to full code we used in this session:
[https://code.earthengine.google.com/84027208bf2a94e77b5f14075fc5a938](https://code.earthengine.google.com/84027208bf2a94e77b5f14075fc5a938)
[https://code.earthengine.google.com/82d2ce3f4927e6894969dc1e0e264d71](https://code.earthengine.google.com/82d2ce3f4927e6894969dc1e0e264d71)


*Adapted from the [Earth Engine 201 Intermediate workshop](https://developers.google.com/earth-engine/classification)*
Expand Down Expand Up @@ -108,13 +108,20 @@ var training = composite.select(bands).sampleRegions({
The `FeatureCollection` called **training** has the reflectance value from each band stored for every training point along with its class label.

### Train the classifier
We will now instantiate a `classifier` using `ee.Classifier.randomForest()` and `train` it on the training data specifying the features to use (training), the landcover categories as the `classProperty` we want to categorize the imagery into, and the reflectance in B2 - B7 of the Landsat imagery as the `inputProperties`.
We will now instantiate a `classifier` using `ee.Classifier.smileRandomForest()` and `train` it on the training data specifying the features to use (training), the landcover categories as the `classProperty` we want to categorize the imagery into, and the reflectance in B2 - B7 of the Landsat imagery as the `inputProperties`.

[About smileRandomForest](https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest)

{% highlight javascript %}
// Make a Random Forest classifier and train it.
var classifier = ee.Classifier.randomForest().train({
features: training,
classProperty: 'landcover',
// Make a SmileRandom Forest classifier and train it.
var classifier = ee.Classifier.smileRandomForest({
numberOfTrees: 100,
minLeafPopulation: 1,
bagFraction: 0.5,
maxNodes: 100
}).train({
features: training,
classProperty: 'landcover',
inputProperties: bands
});
{% endhighlight %}
Expand Down