GEE Tutorial #33 - Performing Accuracy Assessment for Image Classification (Random Forest)

[@dannypan4143]

Very useful! Please keep going.

[@ruangbhumi]

I got confused about accuracy assessment and validation. What is the difference? do we need to do both? usually, I just do the accuracy assessment with different points that I use for classification. Hope you could explain about it. Anyway thank you for your great tutorials, I learn a lot from it.

[@erickabuchanga6759]

Very informative

[@jhonericauntaduarte4072]

very usefull professor Wu. I would like to ask you, How can I do a similar process but with an external source of points as reference for accuraccy assessment?

[@sedgeleyp]

What is the significance behind calculating the two matrices (train accuracy and validation)? what are the comparison criteria?

[@asrafulalam4699]

Very very useful sir. I want to know how the random forest model apply in suspended sediment concentration measurements ?

[@ritikaprasai5118]

Upto what sized raster we can train and extract by supervised algorithms on gge? I tried to train 9 GB, but it gives the error.

[@giswqs]

What error? See developers.google.com/earth-engine/guides/classification

[@ritikaprasai5118]

@@giswqs I am trying to use the boundary (rectangle) geometry to extract the study area and then train the area which is approximately 9 GB in local computer. I could not upload that in my GGE account-it gives me the error so I used its extent to select the same area from the map and then train it. But it only selects a smaller part/chip of it and train the area. When I download the raster- it is only around 178 KB. I kept scale to 1 as I am using NAIP imagery.( I also tried using 17 scale) Would really appreciate your guidance on it. Thank you!

[@giswqs]

@@ritikaprasai5118 How did you select NAIP imagery? See developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ

[@ritikaprasai5118]

@@giswqs
Syntax:
region = ee.Geometry.Rectangle([-85.46293227354874, 30.522437102328066,-84.1487070857995 ,30.08361801814346 ])
dataset = ee.ImageCollection('USDA/NAIP/DOQQ').filter(ee.Filter.date('2017-01-01', '2018-12-31')).filterBounds(region).first()
1. Select training samples:-
points = nlcd.sample(**{
'region': dataset.geometry(),
'scale': 17,
'numPixels': 100000,
'seed': 0,
'geometries': True # Set this to False to ignore geometries
})
Map.addLayer(points, {}, 'training', False)
2. Split the sample:-
label = 'landcover'
# Overlay the points on the imagery to get training.
sample = dataset.select(bands).sampleRegions(**{
'collection': points,
'properties': [label],
'scale': 17
})
# Adds a column of deterministic pseudorandom numbers.
sample = sample.randomColumn()
split = 0.7
training = sample.filter(ee.Filter.lt('random', split))
validation = sample.filter(ee.Filter.gte('random', split))
3. Classifier:-
classifier = ee.Classifier.smileRandomForest(10).train(training, label, bands)
Export the result:-
import os
out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')
out_file = os.path.join(out_dir, 'landcoverfinalfinalforNAIP.tif')
geemap.ee_export_image_to_drive(landcover, description='landcover', folder='export', scale=1)
These are the major syntax used for the classification. Can we export the result around 9 GB directly to our local computer without having to store in google drive? I tried that also but did not help.

[@aminabdo9760]

Great one as usual from my Sifu.
Please prof. For better accuracy do you recommend training pixels with points or polygons.
Thanks

[@satriobudi6090]

can you project another country

[@giswqs]

Yes, you can.