update

code/end_to_end_gee/02-Earth-Engine-Intermediate/03b_Computation_on_Image_Collections_(complete)

@@ -24,12 +24,12 @@ var withNdvi = filtered.map(addNDVI);
 
 var composite = withNdvi.median();
 
-var ndviComposite = composite.select('ndvi').clip(geometry);
+var ndviComposite = composite.select('ndvi');
 
 var palette = [
   'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
   '74A901', '66A000', '529400', '3E8601', '207401', '056201',
   '004C00', '023B01', '012E01', '011D01', '011301'];
 
 var ndviVis = {min:0, max:0.5, palette: palette };
-Map.addLayer(ndviComposite, ndviVis, 'ndvi');
+Map.addLayer(ndviComposite.clip(geometry), ndviVis, 'ndvi');

code/end_to_end_gee/02-Earth-Engine-Intermediate/03c_Computation_on_Image_Collections_(exercise)

@@ -25,10 +25,11 @@ function addIndices(image) {
 var withIndices = filtered.map(addIndices);
 
 // Composite
-var composite = withIndices.median().clip(geometry);
+var composite = withIndices.median();
 print(composite);
 
-// Extract the 'ndwi' band and display a NDWI map
+// Extract the 'ndwi' band
+// Clip and display a NDWI map
 // use the palette ['white', 'blue']
 // Hint: Use .select() function to select a band
 

code/end_to_end_gee/03-Supervised-Classification/01b_Basic_Supervised_Classification_(complete)

@@ -229,10 +229,10 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry) ;
+var composite = filtered.median();
 
 // Display the input composite.
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 var gcps = urban.merge(bare).merge(water).merge(vegetation);
 
@@ -258,6 +258,6 @@ Map.centerObject(geometry);
 // Urban, Bare, Water, Vegetation
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
 
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 
 

code/end_to_end_gee/03-Supervised-Classification/01c_Basic_Supervised_Classification_(exercise)

@@ -16,12 +16,12 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry);
+var composite = filtered.median();
 
 // Display the input composite.
 
 var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 // Exercise
 // Add training points for 4 classes
@@ -60,6 +60,6 @@ Map.addLayer(composite, rgbVis, 'image');
 // // Urban, Bare, Water, Vegetation
 // var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
 
-// Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+// Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 
 

code/end_to_end_gee/03-Supervised-Classification/01d_Basic_Supervised_Classification_(noimport)

@@ -15,15 +15,15 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry);
+var composite = filtered.median();
 
 // Display the input composite.
 var rgbVis = {
   min: 0.0,
   max: 3000,
   bands: ['B4', 'B3', 'B2'],
 };
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 var gcps = urban.merge(bare).merge(water).merge(vegetation);
 
@@ -48,7 +48,7 @@ var classified = composite.classify(classifier);
 // Urban, Bare, Water, Vegetation
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
 
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 // Display the GCPs
 // We use the style() function to style the GCPs
 var palette = ee.List(palette);

code/end_to_end_gee/03-Supervised-Classification/02a_Accuracy_Assessment

@@ -19,10 +19,10 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry);
+var composite = filtered.median();
 
 // Display the input composite.
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 
 // Overlay the point on the image to get training data.
@@ -44,4 +44,4 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');

code/end_to_end_gee/03-Supervised-Classification/02b_Accuracy_Assessment_(complete)

@@ -18,10 +18,10 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry);
+var composite = filtered.median();
 
 // Display the input composite.
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 
 // Add a random column and split the GCPs into training and validation set
@@ -53,7 +53,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 //************************************************************************** 
 // Accuracy Assessment
 //************************************************************************** 

code/end_to_end_gee/03-Supervised-Classification/03a_Improving_the_Classification

@@ -20,10 +20,10 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-var composite = filtered.median().clip(geometry) 
+var composite = filtered.median();
 
 // Display the input composite.
-Map.addLayer(composite, rgbVis, 'image');
+Map.addLayer(composite.clip(geometry), rgbVis, 'image');
 
 
 // Add a random column and split the GCPs into training and validation set
@@ -55,7 +55,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 
 //************************************************************************** 
 // Accuracy Assessment

code/end_to_end_gee/03-Supervised-Classification/03b_Improving_the_Classification_(complete)

@@ -35,7 +35,7 @@ var filtered = s2
   .map(maskCloudAndShadowsSR)
   .select('B.*');
 
-var composite = filtered.median().clip(geometry);
+var composite = filtered.median();
 
 
 var addIndices = function(image) {
@@ -62,7 +62,7 @@ var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');
 var composite = composite.addBands(elev).addBands(slope);
 
 var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
-Map.addLayer(composite, visParams, 'RGB');
+Map.addLayer(composite.clip(geometry), visParams, 'RGB');
 
 // Normalize the image 
 
@@ -130,7 +130,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 
 //************************************************************************** 
 // Accuracy Assessment

code/end_to_end_gee/03-Supervised-Classification/04a_Exporting_Classification_Results

@@ -34,7 +34,7 @@ var filtered = s2
   .map(maskCloudAndShadowsSR)
   .select('B.*');
 
-var composite = filtered.median().clip(geometry) ;
+var composite = filtered.median();
 
 var addIndices = function(image) {
   var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
@@ -60,7 +60,7 @@ var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');
 var composite = composite.addBands(elev).addBands(slope);
 
 var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
-Map.addLayer(composite, visParams, 'RGB');
+Map.addLayer(composite.clip(geometry), visParams, 'RGB');
 
 // Normalize the image 
 
@@ -129,7 +129,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 //************************************************************************** 
 // Accuracy Assessment
 //************************************************************************** 

code/end_to_end_gee/03-Supervised-Classification/04b_Exporting_Classification_Results_(complete)

@@ -34,7 +34,7 @@ var filtered = s2
   .map(maskCloudAndShadowsSR)
   .select('B.*');
 
-var composite = filtered.median().clip(geometry) ;
+var composite = filtered.median();
 
 var addIndices = function(image) {
   var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
@@ -60,7 +60,7 @@ var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');
 var composite = composite.addBands(elev).addBands(slope);
 
 var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
-Map.addLayer(composite, visParams, 'RGB');
+Map.addLayer(composite.clip(geometry), visParams, 'RGB');
 
 // Normalize the image 
 
@@ -129,7 +129,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 
 //************************************************************************** 
 // Accuracy Assessment

code/end_to_end_gee/03-Supervised-Classification/04c_Exporting_Classification_Results_(exercise)

@@ -34,7 +34,7 @@ var filtered = s2
   .map(maskCloudAndShadowsSR)
   .select('B.*');
 
-var composite = filtered.median().clip(geometry) ;
+var composite = filtered.median();
 
 var addIndices = function(image) {
   var ndvi = image.normalizedDifference(['B8', 'B4']).rename(['ndvi']);
@@ -60,7 +60,7 @@ var slope = ee.Terrain.slope(alos.select('AVE_DSM')).rename('slope');
 var composite = composite.addBands(elev).addBands(slope);
 
 var visParams = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.2};
-Map.addLayer(composite, visParams, 'RGB');
+Map.addLayer(composite.clip(geometry), visParams, 'RGB');
 
 // Normalize the image 
 
@@ -129,7 +129,7 @@ var classifier = ee.Classifier.smileRandomForest(50)
 var classified = composite.classify(classifier);
 
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
-Map.addLayer(classified, {min: 0, max: 3, palette: palette}, '2019');
+Map.addLayer(classified.clip(geometry), {min: 0, max: 3, palette: palette}, '2019');
 // Exercise 
 
 // Use the Export.image.toAsset() function to export the 

code/end_to_end_gee/04-Change-Detection/03a_Classifying_Change

@@ -188,15 +188,15 @@ var filtered = s2
   .map(maskS2clouds);
 
 
-var image2019 = filtered.median().clip(geometry);
+var image2019 = filtered.median();
 // Display the input composite.
-Map.addLayer(image2019, rgbVis, '2019');
+Map.addLayer(image2019.clip(geometry), rgbVis, '2019');
 
 var filtered = s2
   .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
   .filter(ee.Filter.bounds(bangalore))
   .map(maskS2clouds)
 
-var image2020 = filtered.median().clip(geometry);
+var image2020 = filtered.median();
 
-Map.addLayer(image2020, rgbVis, '2020');
+Map.addLayer(image2020.clip(geometry), rgbVis, '2020');

code/end_to_end_gee/04-Change-Detection/03b_Classifying_Change_(complete)

@@ -29,18 +29,18 @@ var filtered = s2
   .map(maskS2clouds);
 
 
-var image2019 = filtered.median().clip(geometry);
+var image2019 = filtered.median();
 // Display the input composite.
-Map.addLayer(image2019, rgbVis, '2019');
+Map.addLayer(image2019.clip(geometry), rgbVis, '2019');
 
 var filtered = s2
   .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
   .filter(ee.Filter.bounds(bangalore))
   .map(maskS2clouds);
 
-var image2020 = filtered.median().clip(geometry);
+var image2020 = filtered.median();
 
-Map.addLayer(image2020, rgbVis, '2020');
+Map.addLayer(image2020.clip(geometry), rgbVis, '2020');
 
 var stackedImage = image2019.addBands(image2020);
 
@@ -60,4 +60,4 @@ var classifier = ee.Classifier.smileRandomForest(50).train({
 
 // Classify the image.
 var classified = stackedImage.classify(classifier);
-Map.addLayer(classified, {min: 0, max: 1, palette: ['white', 'red']}, 'change'); 
+Map.addLayer(classified.clip(geometry), {min: 0, max: 1, palette: ['white', 'red']}, 'change'); 

code/end_to_end_gee/04-Change-Detection/03c_Classifying_Change_(exercise)

@@ -30,18 +30,18 @@ var filtered = s2
   .map(maskS2clouds);
 
 
-var image2019 = filtered.median().clip(geometry);
+var image2019 = filtered.median();
 // Display the input composite.
-Map.addLayer(image2019, rgbVis, '2019');
+Map.addLayer(image2019.clip(geometry), rgbVis, '2019');
 
 var filtered = s2
   .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
   .filter(ee.Filter.bounds(bangalore))
   .map(maskS2clouds);
 
-var image2020 = filtered.median().clip(geometry);
+var image2020 = filtered.median();
 
-Map.addLayer(image2020, rgbVis, '2020');
+Map.addLayer(image2020.clip(geometry), rgbVis, '2020');
 
 // Exercise
 
@@ -74,4 +74,4 @@ var classifier = ee.Classifier.smileRandomForest(50).train({
 
 // Classify the image.
 var classified = stackedImage.classify(classifier);
-Map.addLayer(classified, {min: 0, max: 1, palette: ['white', 'red']}, 'change'); 
+Map.addLayer(classified.clip(geometry), {min: 0, max: 1, palette: ['white', 'red']}, 'change'); 

code/end_to_end_gee/04-Change-Detection/04a_Post_Classification_Comparison

@@ -249,10 +249,9 @@ var filtered = s2
   .filter(ee.Filter.bounds(geometry))
   .select('B.*');
 
-
-var before = filtered.median().clip(geometry);
+var before = filtered.median();
 // Display the input composite.
-Map.addLayer(before, rgbVis, 'before');
+Map.addLayer(before.clip(geometry), rgbVis, 'before');
 
 var training = urban.merge(bare).merge(water).merge(vegetation);
 
@@ -274,21 +273,20 @@ var classifier = ee.Classifier.smileRandomForest(50).train({
 var beforeClassified = before.classify(classifier);
 var palette = ['#cc6d8f', '#ffc107', '#1e88e5', '#004d40' ];
 var classifiedVis = {min: 0, max: 3, palette: palette};
-Map.addLayer(beforeClassified, classifiedVis, 'before_classified');
+Map.addLayer(beforeClassified.clip(geometry), classifiedVis, 'before_classified');
 
 
 // 2020 Jan
 var after = s2
   .filter(ee.Filter.date('2020-01-01', '2020-02-01'))
   .filter(ee.Filter.bounds(geometry))
   .select('B.*')
-  .median()
-  .clip(geometry);
-
+  .median();
+
 Map.addLayer(after, rgbVis, 'after');
 
 // Classify the image.
 var afterClassified= after.classify(classifier);
-Map.addLayer(afterClassified, classifiedVis, 'after_classified');
+Map.addLayer(afterClassified.clip(geometry), classifiedVis, 'after_classified');