To do this, we’re going to train a neural network called UNet (short for “Upsampling Network”) on some labeled training data. This means feeding it images with known tree locations and having it learn how to predict where the trees are in new, unlabeled images. The cool thing about UNet is that it can handle both small-scale details (like individual trees) as well as larger-scale patterns (like forest clearings).
Once we’ve trained our model, we can use it to map out deforestation over time by comparing the results from different satellite images. For example, if we have an image from 2015 and another one from 2020 that covers the same area, we can feed them both through UNet and see how much of the forest has been cleared in between.
Here’s a simplified version of what the code might look like:
# Load training data (assuming you have a dataset called "train")
import pandas as pd # importing the pandas library to work with dataframes
from sklearn.model_selection import train_test_split # importing the train_test_split function from sklearn to split data into train and test sets
df = pd.read_csv("train.csv") # reading the csv file and storing it in a dataframe called "df"
X = df["image"].values # extracting the "image" column from the dataframe and storing it as a numpy array in "X"
y = df[["x", "y"]].values # extracting the "x" and "y" columns from the dataframe and storing them as a numpy array in "y"
# Split data into train/test sets
train_indices, test_indices = train_test_split(np.arange(len(X)), test_size=0.2, random_state=42) # splitting the data into train and test sets with a 80/20 split and a random state of 42 for reproducibility
X_train = X[train_indices] # selecting the images for the training set using the train indices
y_train = y[train_indices] # selecting the corresponding coordinates for the training set using the train indices
X_test = X[test_indices] # selecting the images for the test set using the test indices
y_test = y[test_indices] # selecting the corresponding coordinates for the test set using the test indices
# Train UNet model on the train set
from unet import UNet # importing the UNet model from the unet library
model = UNet() # creating an instance of the UNet model
model.compile(optimizer="adam", loss="binary_crossentropy") # compiling the model with the Adam optimizer and binary crossentropy loss function
history = model.fit(X_train, y_train, epochs=10) # training the model on the training set for 10 epochs (iterations through the data) and storing the training history in "history"
And that’s it! Once you have a trained UNet model, you can use it to predict tree locations in new images and track deforestation over time. Pretty cool, huh?