This application uses a transformer-based ChangeFormer model to detect deforestation in the Brazilian Amazon using Sentinel-2 satellite imagery. Developed as a final year project, it processes 4-band (RGB + NIR) .tif images from 2020 and 2021 to generate binary change masks and overlay predictions, achieving an F1-score of 0.9886 and IoU of 0.9572 on validation data.

• Model: Custom ChangeFormer with a VisionTransformer encoder, FeatureDifferenceModule, and DeconvDecoder.
• Data: Sentinel-2 Level-2A imagery (10m resolution) and PRODES ground-truth data.
• Interface: Built with Gradio for interactive uploads and visualizations.
• Purpose: Supports land governance, policy-making, and ecological conservation through scalable deforestation monitoring.

• Upload two .tif images (2020 and 2021) with 4 bands (B2, B3, B4, B8).
• Outputs: Raw 2021 RGB, overlay with predicted deforestation, binary change mask, and a comment on change percentage.
• Patch-wise processing for large images, with percentile-based normalization and stitching.

1. Prerequisites:

   • Python 3.8+
   • Required libraries: torch, torchvision, timm, rasterio, numpy, pillow, gradio.

2. Installation:

   • Clone or download this repository.
   • Install dependencies:

     pip install -r requirements.txt

   • Place your pretrained model (best_model.pth) in the models/ folder.

3. Run Locally:

   • Launch the app:

     python app.py

   • Access the interface at http://localhost:7860.

4. Deployed Version:

   • Check the live app at ***** .

• Input: Upload two .tif files (e.g., 256x256 patches) containing RGB and NIR bands.
• Output:
  • Raw 2021 RGB: Normalized base image.
  • Overlay with Prediction: Red overlay highlighting deforested areas.
  • Binary Change Mask: Black-and-white change map.
  • Comment: Auto-generated note on change extent (e.g., "Significant change detected: 5.83%").
• Notes: Ensure images are preprocessed (e.g., <20% cloud cover) for best results.

• Region of Interest: Top 5 deforested conservation units (e.g., Área de Proteção Ambiental Triunfo do Xingu).
• Dataset: 19,560 bitemporal patches (2020–2021), augmented with rotations.
• Performance: Validation F1-score: 0.9986, IoU: 0.9972.
• Future Work: Multi-year forecasting, web-based alerts, SAR integration.

• Author: Emmanuel Amey, Sammuel Young Appiah, Asare Prince Owusu, Yaaya Pearl Apenu.
• References: Inspired by Alshehri et al. (2024), IEEE Geoscience and Remote Sensing Letters.