Industrial Shed Detection Supervised Learning.txt

##code link : https://code.earthengine.google.com/88d40c868e5cab0a7bd6e0b58eb2729d
var point = /* color: #d63000 */ee.Geometry.Point([93.33984375, 29.240873709577276]),
    l8 = ee.ImageCollection("LANDSAT/LC8_L1T_TOA"),
    area = /* color: #3fd60e */ee.Geometry.Polygon(
        [[[92.60588640810738, 28.208223243210448],
          [92.91333900173231, 28.266260308126974],
          [93.56118389649578, 28.70055871373618],
          [94.28587943734033, 28.969887967567693],
          [94.25293648610159, 29.15224608816576],
          [94.81292207987894, 29.28641636858967],
          [95.24115377126463, 29.113899725149192],
          [95.24112970574947, 29.792723999564156],
          [93.60510809407515, 30.002116035922104],
          [92.32045086886671, 29.945064007516525],
          [91.9470958920067, 29.065930562678464]]]),
    sent = ee.ImageCollection("COPERNICUS/S2");
	
// setting the map to Calicut 
Map.setCenter(90.64,29.18, 9);


// Buffer region 
var roi = point.buffer(90000);
Map.addLayer(roi);

// Bands to select from the image
var bands = ['B1','B2', 'B3', 'B4', 'B5', 'B7','B10','B11'];

// Visualization parameters
var viz = {bands: ['B3', 'B2', 'B1'], max: 0.5, gamma: 2};

// Getting best image tile for the area near point
var image = ee.Image(sent.filterBounds(point).filterDate('2016-01-01','2017-09-01').sort('CLOUDY_PIXEL_PERCENTAGE').first()).select(bands);

// clipping the district boundary from image tile
image = image.clip(area);

// adding the best clipped image for the district
Map.addLayer(image,viz,'tibet');

// Passing training data for the classifier
var polygons = ee.FeatureCollection('ft:1EMZP2--JEUSmrzdLY3QHHiqmuu5ovNnzTUquKTMZ').remap([1, 2], [0, 1], 'class');

// creating training variable to be passed to the classifier
var training = image.sampleRegions({

  collection: polygons,
  properties: ['class'],
  scale: 30
});

// training for the classifier ( Random Forest classifier used with 10 trees )
var trained = ee.Classifier.randomForest(20).train(training, 'class', bands);

// classifying the whole image for the district out of the training that classifier got
var classified = image.classify(trained);

// adding the classified image to the map
Map.addLayer(classified.updateMask(classified.not()), {min: 0, max: 1, palette: ['FF0000', '00FF00']},
  'shed detected');