Sentinel-2 Random Forest Classifier

Description of the algorithm

This project showcases how to train and apply a Random Forest model for the classification of Sentinel-2 imagery.

The workflow is intended to serve as recipe for the developement of Prediction models (Regression and Classification) based on Machine Learning algorithms (Random Forest, KNN, SVM, K-means...) in a Geospatial context.

The workflow covers the following operations:

• Inegrate geospatial input Dataset: satellite imagery in this case. Other layers can be added depending on the project.
• Prepare the Dataset for ML: format Features (X) and Tragets/Labels (y) into Arrays
• Split the Dataset into Train and Test subsets
• Train the Model: fit a model using the train dataset
• Evaluate the model: assess the model accuracy by predicting the test dataset
• Apply the model

Required packages

The following packages need to be installed on your Python Env to run the this code:

• Numpy
• Pandas
• Osgeo (gdal and ogr)
• Scikit-learn

import os
import numpy as np
import pandas as pd
from osgeo import ogr
from osgeo import gdal, gdal_array
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

User inputs

Running this script requires the folowing user inputs:

1. workspace (folder): directory where the script will be executed.

2. Imagery path (folder): the SAFE (Standard Archive Format for Europe) directory containing the Sentine-2 imagery. e.g. S2B_MSIL2A_20191208T184749_N0213_R070_T11UNQ_20191208T205518.SAFE

3. Training dataset path (shapefile): The Polgyon Shapefile (.shp) containing the training areas. The shp file must have an "Id" field containing unique Class IDs.

#-------------------------------------------------------#
# Run the algorithm
#-------------------------------------------------------#

#Define paths to input data (imagery and training dataset)
workspace = r'C:\...\workspace'
imagery_folder = r'C:\...\S2B_MSIL2A_20191208T184749_N0213_R070_T11UNQ_20191208T205518.SAFE'
roi_shp_path = os.path.join (workspace, 'inputs','training_data.shp')

#Run
def main():
    imgComp = MakeImageComposite (imagery_folder)
    roi_raster = CreateROIraster (imgComp, roi_shp_path)
    X,y = PrepareArrays (roi_raster)
    model = TrainModel (X,y)
    ApplyModel (model)
    print ('Processing Completed!') 

if __name__ == "__main__":
    main()

Example training data shp is provided in the test_data folder

Contributions

All contributions are welcome.