In some cases, hazy dispersed clouds are incorrectly classified as
-off-shore sediment. Caution should be used clouds characterize a large
+
In many cases, cirrus clouds are incorrectly classified as off-shore
+sediment. Caution should be used when clouds characterize a large
proportion of the AOI.
+
References
diff --git a/modeling/04_Landsat_5_GTB.Rmd b/modeling/04_Landsat_5_GTB.Rmd
index 2d7733e..1c35788 100644
--- a/modeling/04_Landsat_5_GTB.Rmd
+++ b/modeling/04_Landsat_5_GTB.Rmd
@@ -100,12 +100,16 @@ trainedGTB_ls5 = (ee.Classifier.smileGradientTreeBoost(10).train(
classProperty = 'byte_property',
inputProperties = ls_input_feat
))
-```
-Unfortunately, there is no current mechanism to save the GTB object, so all models
-may be *slightly* different if re-created, but will likely have very similar
-outcomes.
+print(trainedGTB_ls5.getInfo())
+```
+Unfortunately, there is no current mechanism to save the GTB object. This is a
+bummer because you can't really set a seed for these either, however! GEE is a bit
+more rudimentary and recognizes the inputs and therefore creates the same output
+objects. I did a quick check of this by running the model here and then again
+in the browser. Both have identical versions, so I feel confident that GEE is
+making the 'same' model.
## Evaluate the models
@@ -551,6 +555,30 @@ for d in range(date_length_5):
#Send next task.
export_image.start()
+for d in range(date_length_5):
+ md = uniqueMissDate_l5.get(d)
+ print(md.getInfo())
+ print(str(d+1) + ' of ' + str(date_length_5))
+ image = (newStack_l5
+ .filter(ee.Filter.eq('missDate', md))
+ .first()
+ .clip(aoi_ee.geometry()))
+ image_new_class = (classifications_to_one_band(image)
+ .select('reclass'))
+ export_image = ee.batch.Export.image.toAsset(
+ image = image_new_class,
+ region = aoi_ee.geometry(),
+ description = 'GTB_v' + v_date + '_' + str(md.getInfo()),
+ assetId = 'projects/ee-ross-superior/assets/LS5/'+'GTB_LS5)'+str(md.getInfo())+'_v'+v_date,
+ scale = 30,
+ crs = img_crs,
+ maxPixels = 1e13)
+
+ #Check how many existing tasks are running and take a break of 5 mins if it's >10
+ maximum_no_of_tasks(10, 5*60)
+ #Send next task.
+ export_image.start()
+
```
diff --git a/modeling/05_Landsat_7_GTB.Rmd b/modeling/05_Landsat_7_GTB.Rmd
index e4816d0..e3a5bca 100644
--- a/modeling/05_Landsat_7_GTB.Rmd
+++ b/modeling/05_Landsat_7_GTB.Rmd
@@ -99,11 +99,16 @@ trainedGTB_ls7 = (ee.Classifier.smileGradientTreeBoost(10).train(
classProperty = 'byte_property',
inputProperties = ls_input_feat
))
+
+print(trainedGTB_ls7.getInfo())
```
-Unfortunately, there is no current mechanism to save the GTB object, so all models
-may be *slightly* different if re-created, but will likely have very similar
-outcomes.
+Unfortunately, there is no current mechanism to save the GTB object. This is a
+bummer because you can't really set a seed for these either, however! GEE is a bit
+more rudimentary and recognizes the inputs and therefore creates the same output
+objects. I did a quick check of this by running the model here and then again
+in the browser. Both have identical versions, so I feel confident that GEE is
+making the 'same' model.
## Evaluate the models
@@ -543,6 +548,30 @@ for d in range(date_length_7):
maximum_no_of_tasks(10, 5*60)
#Send next task.
export_image.start()
+
+for d in range(date_length_7):
+ md = uniqueMissDate_l7.get(d)
+ print(md.getInfo())
+ print(str(d+1) + ' of ' + str(date_length_7))
+ image = (newStack_l7
+ .filter(ee.Filter.eq('missDate', md))
+ .first()
+ .clip(aoi_ee.geometry()))
+ image_new_class = (classifications_to_one_band(image)
+ .select('reclass'))
+ export_image = ee.batch.Export.image.toAsset(
+ image = image_new_class,
+ region = aoi_ee.geometry(),
+ description = 'GTB_v' + v_date + '_' + str(md.getInfo()),
+ assetId = 'projects/ee-ross-superior/assets/LS7/'+'GTB_LS7_'+str(md.getInfo())+'_v'+v_date,
+ scale = 30,
+ crs = img_crs,
+ maxPixels = 1e13)
+
+ #Check how many existing tasks are running and take a break of 5 mins if it's >10
+ maximum_no_of_tasks(10, 5*60)
+ #Send next task.
+ export_image.start()
```
diff --git a/modeling/06_Landsat_8_GTB.Rmd b/modeling/06_Landsat_8_GTB.Rmd
index 6b7227f..7f02cd3 100644
--- a/modeling/06_Landsat_8_GTB.Rmd
+++ b/modeling/06_Landsat_8_GTB.Rmd
@@ -99,11 +99,16 @@ trainedGTB_ls8 = (ee.Classifier.smileGradientTreeBoost(10).train(
classProperty = 'byte_property',
inputProperties = ls_input_feat
))
+
+print(trainedGTB_ls8.getInfo())
```
-Unfortunately, there is no current mechanism to save the GTB object, so all models
-may be *slightly* different if re-created, but will likely have very similar
-outcomes.
+Unfortunately, there is no current mechanism to save the GTB object. This is a
+bummer because you can't really set a seed for these either, however! GEE is a bit
+more rudimentary and recognizes the inputs and therefore creates the same output
+objects. I did a quick check of this by running the model here and then again
+in the browser. Both have identical versions, so I feel confident that GEE is
+making the 'same' model.
## Evaluate the models
@@ -544,6 +549,32 @@ for d in range(date_length_8):
maximum_no_of_tasks(10, 5*60)
#Send next task.
export_image.start()
+
+
+for d in range(date_length_8):
+ md = uniqueMissDate_l8.get(d)
+ print(md.getInfo())
+ print(str(d+1) + ' of ' + str(date_length_8))
+ image = (newStack_l8
+ .filter(ee.Filter.eq('missDate', md))
+ .first()
+ .clip(aoi_ee.geometry()))
+ image_new_class = (classifications_to_one_band(image)
+ .select('reclass'))
+ export_image = ee.batch.Export.image.toAsset(
+ image = image_new_class,
+ region = aoi_ee.geometry(),
+ description = 'GTB_v' + v_date + '_' + str(md.getInfo()),
+ assetId = 'projects/ee-ross-superior/assets/LS8/'+'GTB_LS8_'+str(md.getInfo())+'_v'+v_date,
+ scale = 30,
+ crs = img_crs,
+ maxPixels = 1e13)
+
+ #Check how many existing tasks are running and take a break of 5 mins if it's >10
+ maximum_no_of_tasks(10, 5*60)
+ #Send next task.
+ export_image.start()
+
```
diff --git a/modeling/07_Landsat_9_GTB.Rmd b/modeling/07_Landsat_9_GTB.Rmd
index ae5576d..7919131 100644
--- a/modeling/07_Landsat_9_GTB.Rmd
+++ b/modeling/07_Landsat_9_GTB.Rmd
@@ -99,12 +99,16 @@ trainedGTB_ls9 = (ee.Classifier.smileGradientTreeBoost(10).train(
classProperty = 'byte_property',
inputProperties = ls_input_feat
))
-```
-Unfortunately, there is no current mechanism to save the GTB object, so all models
-may be *slightly* different if re-created, but will likely have very similar
-outcomes.
+print(trainedGTB_ls9.getInfo())
+```
+Unfortunately, there is no current mechanism to save the GTB object. This is a
+bummer because you can't really set a seed for these either, however! GEE is a bit
+more rudimentary and recognizes the inputs and therefore creates the same output
+objects. I did a quick check of this by running the model here and then again
+in the browser. Both have identical versions, so I feel confident that GEE is
+making the 'same' model.
## Evaluate the models
@@ -546,6 +550,31 @@ for d in range(date_length_9):
#Send next task.
export_image.start()
+
+for d in range(date_length_9):
+ md = uniqueMissDate_l9.get(d)
+ print(md.getInfo())
+ print(str(d+1) + ' of ' + str(date_length_9))
+ image = (newStack_l9
+ .filter(ee.Filter.eq('missDate', md))
+ .first()
+ .clip(aoi_ee.geometry()))
+ image_new_class = (classifications_to_one_band(image)
+ .select('reclass'))
+ export_image = ee.batch.Export.image.toAsset(
+ image = image_new_class,
+ region = aoi_ee.geometry(),
+ description = 'GTB_v' + v_date + '_' + str(md.getInfo()),
+ assetId = 'projects/ee-ross-superior/assets/LS9/'+'GTB_LS9_'+str(md.getInfo())+'_v'+v_date,
+ scale = 30,
+ crs = img_crs,
+ maxPixels = 1e13)
+
+ #Check how many existing tasks are running and take a break of 5 mins if it's >10
+ maximum_no_of_tasks(10, 5*60)
+ #Send next task.
+ export_image.start()
+
```
diff --git a/modeling/08_Sentinel2_GTB.Rmd b/modeling/08_Sentinel2_GTB.Rmd
index 76a340f..5376eaa 100644
--- a/modeling/08_Sentinel2_GTB.Rmd
+++ b/modeling/08_Sentinel2_GTB.Rmd
@@ -100,12 +100,16 @@ trainedGTB_sen = (ee.Classifier.smileGradientTreeBoost(10).train(
classProperty = 'byte_property',
inputProperties = sen_input_feat
))
-```
-Unfortunately, there is no current mechanism to save the GTB object, so all models
-may be *slightly* different if re-created, but will likely have very similar
-outcomes.
+print(trainedGTB_sen.getInfo())
+```
+Unfortunately, there is no current mechanism to save the GTB object. This is a
+bummer because you can't really set a seed for these either, however! GEE is a bit
+more rudimentary and recognizes the inputs and therefore creates the same output
+objects. I did a quick check of this by running the model here and then again
+in the browser. Both have identical versions, so I feel confident that GEE is
+making the 'same' model.
## Evaluate the models
@@ -551,4 +555,29 @@ for d in range(date_length_sen):
#Send next task.
export_image.start()
+
+for d in range(date_length_sen):
+ md = uniqueMissDate_sen.get(d)
+ print(md.getInfo())
+ print(str(d+1) + ' of ' + str(date_length_sen))
+ image = (newStack_sen
+ .filter(ee.Filter.eq('missDate', md))
+ .first()
+ .clip(aoi_ee.geometry()))
+ image_new_class = (classifications_to_one_band(image)
+ .select('reclass'))
+ export_image = ee.batch.Export.image.toAsset(
+ image = image_new_class,
+ region = aoi_ee.geometry(),
+ description = 'GTB_v' + v_date + '_' + str(md.getInfo()),
+ assetId = 'projects/ee-ross-superior/assets/sen/'+'GTB_sen_'+str(md.getInfo())+'_v'+v_date,
+ scale = 30,
+ crs = img_crs,
+ maxPixels = 1e13)
+
+ #Check how many existing tasks are running and take a break of 5 mins if it's >10
+ maximum_no_of_tasks(10, 5*60)
+ #Send next task.
+ export_image.start()
+
```
\ No newline at end of file