Landslide Segmentation Using U-Net and Satellite Imagery

Dataset Loading & Visualization:

• Satellite images and their corresponding masks are stored in .h5 format.
• h5py is used to read and visualize the dataset. The images consist of multiple channels including RGB, NIR (near-infrared), NDVI (Normalized Difference Vegetation Index), slope, and elevation.
• Sample images and masks are displayed using matplotlib

Data Preprocessing:

• The dataset is preprocessed by normalizing the RGB, slope, and elevation channels.
• Any NaN values in the dataset are replaced with small values (e.g., 0.000001).
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• Dataset Splitting:

  • The dataset is split into training and validation sets using train_test_split from sklearn.
  • Data is then structured into PyTorch Dataset and DataLoader objects to facilitate batching during training.
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Model Architecture (U-Net):

• A U-Net model is defined with multiple convolutional and downscaling blocks (DoubleConv, Down, Up).
• The model is designed to take 6-channel inputs and output a binary segmentation mask.
• The U-Net architecture is implemented using PyTorch and includes dropout regularization to prevent overfitting.
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Loss Function:

• A combination of binary cross-entropy loss and Dice loss is used for training.
• Dice loss helps improve performance on imbalanced datasets by focusing on overlapping areas between predicted and true masks.
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Training Loop:

• The model is trained for 30 epochs using the Adam optimizer and the combined loss function.
• Training and validation losses are recorded, and the model with the lowest validation loss is saved as best_model.pth.
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Model Evaluation:

• After training, the model is evaluated using accuracy, precision, recall, and F1 score metrics.
• Predictions are generated on the validation set, and these metrics are computed by comparing predicted masks with ground truth masks.
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Visualization:

• A random sample from the validation set is selected, and its input image, true mask, and predicted mask are visualized side by side using matplotlib.