Session 1 Classification_GEE_Exercice_on_GEE

// This exercice can be accessed at: https://code.earthengine.google.com/c5f6321229fb56c71bdc1c3b5ce544a7

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//S. Aparício (Solenix), I. Alonso (Solenix)  ☁☁☁\_(ツ)_/☁☁☁
//MIT LICENSE CC BY EUMETSAT
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//🔥🛰️ 
// Session 1 “Hands-on: Classification of fire burns"
// PARTICIPANT'S VERSION

///////////////////////////////////////////////////////////////////////////
//////////////////////  1. VISUALIZING DEFINED AOI           ////////////////
///////////////////////////////////////////////////////////////////////////
//______________
//_**STEP 1**___

var AOI = // *your code
//Visualise it
// *your code

///////////////////////////////////////////////////////////////////////////
//////////////   2. PREPARING SATELLITE IMAGERY     /////////////////////////
///////////////////////////////////////////////////////////////////////////
//______________
//_**STEP 2**___

//Selecting date range - after the fire
var start = //* your code
var finish = //* your code

//Selecting date range - before the fire
var startpast = //* your code
var finishpast = //* your code

////////////// CREATING COLLECTION OF IMAGES///////////////////////////////
//______________
//_**STEP 3**___

//Sentinel-2 collection after the fire
var S2after = //* your code
        
//Sentinel-2 collection before the fire
var S2before = //* your code

////////////// CLOUD MASK FUNCTION ///////////////////////////////
//______________
//_**STEP 4**___


//Cloud mask S2 function
function maskS2clouds(image) {
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = ee.Number(2).pow(10).int();  //Math.pow(2,10)
  var cirrusBitMask = ee.Number(2).pow(11).int(); //Math.pow(2,11)

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
             qa.bitwiseAnd(cirrusBitMask).eq(0));

  // Return the masked and scaled data.
  return image.updateMask(mask); // add >>>> .divide(10000) >>>Diving by 10000 converts toa
}


/////////// APPLY CLOUD MASK FUNCTION TO ALL IMAGES IN THE COLLECTION///////////////////////////////
//______________
//_**STEP 5**___

//Applying the function to remove clouds to all the collection
var S2Acloudfree = //* your code
var S2Bcloudfree = //* your code

/////////// CREATE A SINGLE IMAGE ///////////////////////////////
//______________
//_**STEP 6**___

//Creating a single image with the median of the collection
var S2A = //* your code
var S2B = //* your code


/////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////    VISUALIZING SATELLITE IMAGERY     /////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////////
//______________
//_**STEP 7**___

//Creating visualization settings for false color
var False_vis = {
  min: 0,
  max: 2500,
  gamma: 1.4,
  bands: ['B8', 'B4', 'B3']
};

//Creating visualization settings for true color
var rgb_vis = {
  min: 0,
  max: 2500,
  gamma: 0.89,
  bands: ['B4', 'B3', 'B2']
};


Map.addLayer(S2A.clip(AOI), False_vis, 'S2 After Fire (False Color)')
Map.addLayer(S2B.clip(AOI), False_vis, 'S2 Before Fire (False Color)')

Map.addLayer(S2A.clip(AOI), rgb_vis, 'S2 After Fire (True Color)')
Map.addLayer(S2B.clip(AOI), rgb_vis, 'S2 Before Fire (True Color)')

/////////////////////////////////////////////////////////////////////////////////////////////////////
////////////// CREATING TRAINING DATASET ////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////////
//______________
//_**STEP 8**___

//collect more training data on geometry imports>burn/noburn and selecting on the map


//combine the polygons from both classes
var polygons = //**your code

//selection of the bands to be used
var bands = //**your code


//______________
//_**STEP 9**___
//collecting training data
var training = //**your code

// Roughly 80% training, 20% testing.
var split = 0.8;
//Spliting with the split value after assigned random values
var trainingRandom = training.randomColumn('random');

var trainingSplit = trainingRandom.filter(ee.Filter.lt('random', split));    //for classification
var testingSplit = trainingRandom.filter(ee.Filter.gte('random', split));    //for validation and accuracy

///////////////////////////////////////////////////////////////////////////////////////////////////////
////////////// TRAINING THE MODEL /////////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////////
//______________
//_**STEP 10**___

// Training classifiers with training (splited) data
var trainedRF = //**your code


/////////////////////////////////////////////////////////////
////////////// RUN CLASSIFICATION  ///////////////////////////
////////////////////////////////////////////////////////////
//______________
//_**STEP 11**___

// Classify the Image with the same bands used for training.
var classified   = //**your code



/////////////////////////////////////////////////////////////
////////////// vISUALIZE RESULTING CLASSIFICATION  /////////
////////////////////////////////////////////////////////////
//______________
//_**STEP 12**___

var visClassRFS1S2 = classified.visualize({
                    min:0, 
                    max:2, 
                    palette: ['E6E600','A6FFE6']
});


Map.addLayer(classified.clip(AOI), {min: 0, max: 1, palette: ['green','red']}, 'RandomForest Burnt'); 

/*

var burnS2 = S2A.normalizedDifference(['B11', 'B8']);
Map.addLayer(burnS2.clip(PT), {}, 'Burnt Radio ');

*/