amanbasu / wildfire-detection

Using Vision Transformers for enhanced wildfire detection in satellite images

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

wildfire-detection's Issues

other model code

hello,excellent work. May I ask if the codes of trans_Unet and sim_Unet can be published

weight

Could you please upload weight?