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--- |
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license: mit |
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language: en |
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tags: |
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- change-detection |
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- satellite-imagery |
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- deforestation |
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- transformer |
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- gradio |
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- final-year-project |
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datasets: |
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- sentinel-2 |
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model-index: |
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- name: ChangeFormer |
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results: |
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- task: |
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type: image-segmentation |
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name: Change Detection |
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dataset: |
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name: Brazilian Amazon (2020–2021) |
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type: remote-sensing |
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metrics: |
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- type: f1 |
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value: 0.9986 |
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- type: iou |
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value: 0.9972 |
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--- |
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# 🛰️ Deforestation Detection App |
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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 0.9986** and **IoU of 0.9972** on validation data. |
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--- |
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## 🌍 Overview |
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- **Model**: Custom `ChangeFormer` with a Vision Transformer encoder, Feature Difference Module, and Deconv Decoder. |
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- **Data**: Sentinel-2 Level-2A imagery (10m resolution) and PRODES deforestation labels. |
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- **Interface**: Gradio-powered app for drag-and-drop uploads and real-time visualization. |
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- **Purpose**: Scalable and interpretable tool for land monitoring, policy-making, and conservation. |
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--- |
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## ✨ Features |
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- Upload two `.tif` images (2020 and 2021) with 4 bands: B2 (blue), B3 (green), B4 (red), and B8 (NIR). |
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- Outputs: |
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- ✅ Raw RGB base image (2021) |
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- ✅ Binary change mask (black/white) |
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- ✅ RGB + red overlay for deforested regions |
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- ✅ Comment on % of detected change |
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- Handles large images with patch-based tiling, normalization, and stitching. |
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--- |
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## ⚙️ Setup & Usage |
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### 🔧 Prerequisites |
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- Python 3.8+ |
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- Required libraries: `torch`, `torchvision`, `timm`, `rasterio`, `numpy`, `pillow`, `gradio` |
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### 📦 Installation |
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```bash |
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git clone https://github.com/manuelhorvey/ChangeFormer.git |
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cd ChangeFormer |
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pip install -r requirements.txt |
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```` |
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Place your trained model checkpoint as: |
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``` |
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models/best_model.pth |
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``` |
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### 🚀 Run Locally |
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```bash |
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python app.py |
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``` |
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Then open: [http://localhost:7860](http://localhost:7860) |
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--- |
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## 🧪 Input & Output |
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### Input |
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* Two 4-band `.tif` images from the same area: |
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* One from 2020 |
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* One from 2021 |
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* Patches preferred (e.g., 256×256) |
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### Output |
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* RGB image from 2021 |
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* Red-highlighted deforestation overlay |
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* Binary change mask |
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* Textual feedback on % change |
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> ☁️ *Images with <20% cloud cover yield best accuracy.* |
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--- |
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## 📊 Project Details |
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| Item | Value | |
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| ----------------- | ---------------------------------------------------- | |
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| **Region** | Brazilian Amazon (e.g., APA Triunfo do Xingu) | |
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| **Dataset Size** | 19,560 patches (256×256), 4 channels, 2 years | |
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| **Metrics** | F1-score: 0.9986, IoU: 0.9972 | |
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| **Augmentations** | Rotations (90/180/270), flips | |
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| **Future Plans** | Web-based monitoring alerts, SAR fusion, forecasting | |
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--- |
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## 👥 Authors |
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* Emmanuel Amey |
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* Sammuel Young Appiah |
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* Asare Prince Owusu |
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* Yaaya Pearl Apenu |
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> Based on ideas from Alshehri et al. (2024), IEEE GRSL. |
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--- |
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## 🪪 License |
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This project is licensed under the MIT License. |
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```` |
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--- |
