---
license: mit
language: en
tags:
  - change-detection
  - satellite-imagery
  - deforestation
  - transformer
  - gradio
  - final-year-project
datasets:
  - sentinel-2
model-index:
  - name: ChangeFormer
    results:
      - task:
          type: image-segmentation
          name: Change Detection
        dataset:
          name: Brazilian Amazon (2020–2021)
          type: remote-sensing
        metrics:
          - type: f1
            value: 0.9986
          - type: iou
            value: 0.9972
---

# 🛰️ Deforestation Detection App

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.

---

## 🌍 Overview

- **Model**: Custom `ChangeFormer` with a Vision Transformer encoder, Feature Difference Module, and Deconv Decoder.
- **Data**: Sentinel-2 Level-2A imagery (10m resolution) and PRODES deforestation labels.
- **Interface**: Gradio-powered app for drag-and-drop uploads and real-time visualization.
- **Purpose**: Scalable and interpretable tool for land monitoring, policy-making, and conservation.

---

## ✨ Features

- Upload two `.tif` images (2020 and 2021) with 4 bands: B2 (blue), B3 (green), B4 (red), and B8 (NIR).
- Outputs:
  - ✅ Raw RGB base image (2021)
  - ✅ Binary change mask (black/white)
  - ✅ RGB + red overlay for deforested regions
  - ✅ Comment on % of detected change
- Handles large images with patch-based tiling, normalization, and stitching.

---

## ⚙️ Setup & Usage

### 🔧 Prerequisites

- Python 3.8+
- Required libraries: `torch`, `torchvision`, `timm`, `rasterio`, `numpy`, `pillow`, `gradio`

### 📦 Installation

```bash
git clone https://github.com/manuelhorvey/ChangeFormer.git
cd ChangeFormer
pip install -r requirements.txt
````

Place your trained model checkpoint as:

```
models/best_model.pth
```

### 🚀 Run Locally

```bash
python app.py
```

Then open: [http://localhost:7860](http://localhost:7860)

---

## 🧪 Input & Output

### Input

* Two 4-band `.tif` images from the same area:

  * One from 2020
  * One from 2021
* Patches preferred (e.g., 256×256)

### Output

* RGB image from 2021
* Red-highlighted deforestation overlay
* Binary change mask
* Textual feedback on % change

> ☁️ *Images with <20% cloud cover yield best accuracy.*

---

## 📊 Project Details

| Item              | Value                                                |
| ----------------- | ---------------------------------------------------- |
| **Region**        | Brazilian Amazon (e.g., APA Triunfo do Xingu)        |
| **Dataset Size**  | 19,560 patches (256×256), 4 channels, 2 years        |
| **Metrics**       | F1-score: 0.9986, IoU: 0.9972                        |
| **Augmentations** | Rotations (90/180/270), flips                        |
| **Future Plans**  | Web-based monitoring alerts, SAR fusion, forecasting |

---

## 👥 Authors

* Emmanuel Amey
* Sammuel Young Appiah
* Asare Prince Owusu
* Yaaya Pearl Apenu

> Based on ideas from Alshehri et al. (2024), IEEE GRSL.

---

## 🪪 License

This project is licensed under the MIT License.

````

---