F22a Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F2.2 Accuracy Assessment: Quantifying Classification Quality
#  Checkpoint:   F22a
#  Authors:      Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Import the reference dataset.
data = ee.FeatureCollection("projects/gee-book/assets/F2-2/milan_data")

# Define the prediction bands.
predictionBands = [
    "SR_B1",
    "SR_B2",
    "SR_B3",
    "SR_B4",
    "SR_B5",
    "SR_B6",
    "SR_B7",
    "ST_B10",
    "ndvi",
    "ndwi",
]

# Split the dataset into training and testing sets.
trainingTesting = data.randomColumn()
trainingSet = trainingTesting.filter(ee.Filter.lessThan("random", 0.8))
testingSet = trainingTesting.filter(ee.Filter.greaterThanOrEquals("random", 0.8))

# Train the Random Forest Classifier with the trainingSet.
RFclassifier = ee.Classifier.smileRandomForest(50).train(
    {
        "features": trainingSet,
        "classProperty": "class",
        "inputProperties": predictionBands,
    }
)

# Now, to test the classification (verify model's accuracy),
# we classify the testingSet and get a confusion matrix.
confusionMatrix = testingSet.classify(RFclassifier).errorMatrix(
    {"actual": "class", "predicted": "classification"}
)

# Print the results.
print("Confusion matrix:", confusionMatrix)
print("Overall Accuracy:", confusionMatrix.accuracy())
print("Producers Accuracy:", confusionMatrix.producersAccuracy())
print("Consumers Accuracy:", confusionMatrix.consumersAccuracy())
print("Kappa:", confusionMatrix.kappa())

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F2.2 Accuracy Assessment: Quantifying Classification Quality # Checkpoint: F22a # Authors: Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Import the reference dataset. data = ee.FeatureCollection("projects/gee-book/assets/F2-2/milan_data") # Define the prediction bands. predictionBands = [ "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7", "ST_B10", "ndvi", "ndwi", ] # Split the dataset into training and testing sets. trainingTesting = data.randomColumn() trainingSet = trainingTesting.filter(ee.Filter.lessThan("random", 0.8)) testingSet = trainingTesting.filter(ee.Filter.greaterThanOrEquals("random", 0.8)) # Train the Random Forest Classifier with the trainingSet. RFclassifier = ee.Classifier.smileRandomForest(50).train( { "features": trainingSet, "classProperty": "class", "inputProperties": predictionBands, } ) # Now, to test the classification (verify model's accuracy), # we classify the testingSet and get a confusion matrix. confusionMatrix = testingSet.classify(RFclassifier).errorMatrix( {"actual": "class", "predicted": "classification"} ) # Print the results. print("Confusion matrix:", confusionMatrix) print("Overall Accuracy:", confusionMatrix.accuracy()) print("Producers Accuracy:", confusionMatrix.producersAccuracy()) print("Consumers Accuracy:", confusionMatrix.consumersAccuracy()) print("Kappa:", confusionMatrix.kappa()) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map