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Using Vision Transformers for enhanced wildfire detection in satellite images

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Leveraging Vision Transformers for Enhanced Wildfire Detection and Characterization

In this project, we use the active fire dataset from https://github.com/pereira-gha/activefire (data link) and try to improve over their results. We use two Vision Transformer networks: Swin-Unet and TransUnet, and one CNN-based UNet network. We show that ViT can outperform well-trained and specialized CNNs to detect wildfires on a previously published dataset of LandSat-8 imagery (Pereira et al.). One of our ViTs outperforms the baseline CNN comparison by 0.92%. However, we find our own implementation of CNN-based UNet to perform best in every category, showing their sustained utility in image tasks. Overall, ViTs are comparably capable in detecting wildfires as CNNs, though well-tuned CNNs are still the best technique for detecting wildfire with our UNet providing an IoU of 93.58%, better than the baseline UNet by some 4.58%.

File description

  • UNet.py: Contains the pytorch code for UNet model.
  • evaluate.py: Takes in the model name and evaluates the saved checkpoint on 4 metrics: precision, recall, f-score, and IoU.
  • generator.py: Data generator code.
  • models.py: Returns the instances of different models used in this work.
  • predict.py: Saves the inference result from the a checkpoint file.
  • train.py: Code to train a model.
  • transform.py: Image transforms for data augmentation.

Commands

# Train
python train.py <model-name>
## Example
python train.py unet

# Evaluate
python evaluate.py <model-name>
## Example
python evaluate.py unet

# Save predictions
python predict.py <model-name> <image-path>
## Example
python predict.py unet predictions/unet/

Results

Method Precision Recall F-score IoU
U-Net (10c) 92.90 95.50 94.20 89.00
U-Net (3c) 91.90 95.30 93.60 87.90
U-Net-Light (3c) 90.20 96.50 93.20 87.30
TransUNet 88.46 86.88 87.66 87.49
Swin-Unet 88.28 92.30 90.24 89.93
Our UNet 93.37 93.96 93.67 93.58

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Using Vision Transformers for enhanced wildfire detection in satellite images

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