| | --- |
| | license: apache-2.0 |
| | sdk: docker |
| | pinned: true |
| | short_description: Detect deforestation from satellite imagery. |
| | --- |
| | # 🌳 Deforestation Detection using Satellite Imagery |
| |
|
| | This project aims to **detect deforestation from satellite images** using a **U-Net deep learning model**. It leverages automated satellite data collection, image segmentation techniques, and Earth Engine + TensorFlow pipelines to help environmental organizations monitor illegal deforestation in near real-time. |
| |
|
| | <p align="center"> |
| | <img src="Final Output.png" width="70%"/> |
| | </p> |
| |
|
| | --- |
| |
|
| | ## 📌 Project Objective |
| |
|
| | > **Can an automated model built on satellite imagery help environmental organizations monitor illegal deforestation in near real-time?** |
| |
|
| | We aim to answer this by building a robust deep learning pipeline that: |
| | - Collects satellite imagery for selected regions and years. |
| | - Trains a U-Net segmentation model on forest/non-forest (or deforested) labels. |
| | - Allows users to input coordinates + year and visualize predictions on a Leaflet map. |
| |
|
| | --- |
| |
|
| | ## 🛰️ Data Collection & Preprocessing |
| |
|
| | We use **Google Earth Engine (GEE)** to fetch Sentinel-2 or Landsat-8 imagery and NDVI indices. |
| |
|
| | ### Data Sources: |
| | - **Imagery**: Landsat-8 (30m resolution), optionally Sentinel-2 (10m resolution). |
| | - **Labels**: Derived using NDVI and/or forest loss layers from Global Forest Change dataset. |
| |
|
| | ### Preprocessing: |
| | - Region-wise time-lapse collection. |
| | - NDVI-based labeling: Thresholding NDVI to identify vegetation loss. |
| | - Tiling large images into patches. |
| | - Saving as `.npy` and `.tfrecord` formats for model training. |
| |
|
| | --- |
| |
|
| | ## 🧠 Model Architecture |
| |
|
| | The model uses a **U-Net architecture** implemented in **TensorFlow**, designed for image segmentation tasks. |
| |
|
| | - **Input**: Satellite image patches (RGB or NDVI composite). |
| | - **Output**: Binary mask indicating deforested areas. |
| |
|
| | ### Features: |
| | - Batch normalization and dropout regularization |
| | - Dice coefficient + binary cross-entropy loss |
| | - Data augmentation (rotation, flipping, contrast) |
| |
|
| | --- |
| |
|
| | ## ⚙️ Training |
| |
|
| | ```bash |
| | python train.py --epochs 50 --batch_size 16 --data_dir ./data --save_model ./models/unet_deforestation.h5 |
| | |
| | ``` |
| |
|
| | ## 📈 Evaluation |
| |
|
| | The trained model is evaluated on a held-out test set using the following metrics: |
| |
|
| | - **IoU (Intersection over Union)**: Measures the overlap between predicted deforested area and ground truth. |
| | - **Dice Coefficient**: Especially useful for imbalanced classes. |
| | - **Precision & Recall**: To understand false positives and false negatives. |
| | - **F1 Score**: Harmonic mean of precision and recall. |
| |
|
| |
|
| | --- |
| |
|
| | ## 🌍 Deployment |
| |
|
| | ### Hugging Face Space 🚀 |
| | Visit: [**Deforestation Detection on Hugging Face**](https://huggingface.co/spaces/ojasrohatgi/Deforestation-Detection) |
| |
|
| | Features: |
| | - Interactive Leaflet map to select coordinates |
| | - Year input to analyze temporal deforestation |
| | - On-click image fetching and prediction overlay |
| | - Downloadable prediction masks for offline use |
| |
|
| | ### Backend Logic |
| | The backend takes user inputs (`lat, lon, year`), queries GEE for imagery, preprocesses it into patches, passes it to the trained U-Net model, and returns a combined prediction image with overlays. |
| |
|
| | --- |
| |
|
| | ## 🧾 Project Structure |
| |
|
| | ```bash |
| | Deforestation-Detection/ |
| | │ |
| | ├── app/ # Streamlit-based frontend interface |
| | │ ├── main.py # Streamlit UI & user interaction logic |
| | │ ├── map_component.html # Leaflet map integration for coordinate input |
| | │ ├── overlay.py # Image overlay + visualization helpers |
| | │ └── utils.py # Patching, preprocessing, and GEE interaction |
| | │ |
| | ├── model/ # Trained model artifacts |
| | │ └── unet_deforestation.h5 # Final trained U-Net model |
| | │ |
| | ├── earth_engine/ # Scripts for working with GEE |
| | │ └── export_data.py # Downloads satellite images + NDVI labels |
| | │ |
| | ├── training/ # Model architecture and training logic |
| | │ ├── train.py # Model training loop |
| | │ ├── evaluate.py # Evaluation script for test data |
| | │ ├── metrics.py # IoU, Dice, and other evaluation metrics |
| | │ └── unet_model.py # Custom U-Net implementation |
| | │ |
| | ├── data/ # Data directory (images, masks, TFRecords) |
| | │ ├── train/ # Training image and mask patches |
| | │ ├── val/ # Validation image and mask patches |
| | │ └── test/ # Test image and mask patches |
| | │ |
| | ├── requirements.txt # Python dependencies |
| | ├── README.md # This file |
| | └── .gitattributes / .gitignore # GitHub config files |
| | ``` |
| |
|
| | ## 🧪 How to Run Locally |
| |
|
| | ### 1. Clone the repository |
| |
|
| | ```bash |
| | git clone [https://github.com/Ojas-Rohatgi/Deforestation-Detection](https://github.com/Ojas-Rohatgi/Deforestation-Detection) |
| | cd Deforestation-Detection |
| | |
| | ``` |
| |
|
| | ### 2. Set up a virtual environment |
| |
|
| | ```bash |
| | python -m venv venv |
| | source venv/bin/activate # On Windows: venv\Scripts\activate |
| | pip install -r requirements.txt |
| | |
| | ``` |
| |
|
| | ### 3. Prepare data |
| |
|
| | Ensure you have satellite imagery and labeled NDVI masks stored in the `data/` folder. |
| | You can generate training data using Earth Engine export scripts in `earth_engine/export_data.py`. |
| |
|
| | ### 4. Train the model |
| |
|
| | ```bash |
| | python training/train.py --epochs 50 --batch\_size 16 --data\_dir ./data --save\_model ./model/unet\_deforestation.h5 |
| | |
| | ``` |
| | You can configure the number of epochs, batch size, and save path as needed. |
| |
|
| | ### 5. Run the app |
| |
|
| | ```bash |
| | cd app |
| | streamlit run main.py |
| | |
| | ``` |
| | This will launch a web interface where you can: |
| |
|
| | * Select a region using the interactive Leaflet map |
| | * Input a year (e.g., 2020) |
| | * Fetch satellite imagery and get prediction overlays |
| |
|
| | --- |
| |
|
| | ## ☁️ Deployment |
| |
|
| | This app is live at: |
| | 🔗 [Hugging Face Spaces – ojasrohatgi/Deforestation-Detection](https://huggingface.co/spaces/ojasrohatgi/Deforestation-Detection) |
| |
|
| | ### Features: |
| |
|
| | * 🌍 Interactive map for selecting location |
| | * 📅 Year input for time-based analysis |
| | * 📷 Automatic image fetch via GEE |
| | * 🧠 Real-time inference with trained U-Net |
| | * 🖼️ Visual overlay of predictions |
| | * ⬇️ Option to download the prediction mask |
| |
|
| | --- |
| |
|
| | ## ⚙️ Tech Stack |
| |
|
| | | Component | Tools Used | |
| | | ---------------- | -------------------------------------- | |
| | | Model | TensorFlow 2.x, Keras, U-Net | |
| | | Data Source | Google Earth Engine (Landsat-8, NDVI) | |
| | | Frontend | Flask, Leaflet.js | |
| | | Image Processing | NumPy, OpenCV, Matplotlib, PIL | |
| | | Deployment | Hugging Face Spaces, GitHub | |
| | | Output Formats | PNG masks, NumPy arrays, TFRecords | |
| |
|
| | --- |
| |
|
| | ## 🚀 Future Roadmap |
| |
|
| | * [ ] Multi-year change detection (e.g., forest loss between 2015–2023) |
| | * [ ] Visual heatmaps of deforestation severity |
| | * [ ] Region-specific fine-tuning for Southeast Asia, Amazon, and Africa |
| | * [ ] Support for multispectral and SAR imagery |
| | * [ ] Webhooks/API integration for automated NGO alerts |
| |
|
| | --- |
| |
|
| | ## 🙋♂️ Author |
| |
|
| | **Ojas Rohatgi** |
| | Final Year B.Tech – Computer Science (Data Science & AI) |
| | SRM University, Sonepat, Haryana, India |
| |
|
| | * 🔗 GitHub: [Ojas-Rohatgi](https://github.com/Ojas-Rohatgi) |
| | * 🔗 Hugging Face: [ojasrohatgi](https://huggingface.co/ojasrohatgi) |
| |
|
| | --- |
| |
|
| | ## 📜 License |
| |
|
| | This project is licensed under the **MIT License**. |
| | See the [LICENSE](LICENSE) file for full license text. |
| |
|
| | --- |
| |
|
| | ## ⭐️ Support This Project |
| |
|
| | If you found this useful: |
| |
|
| | * 🌟 Star this repository on GitHub |
| | * 🚀 Share the Hugging Face app with your network |
| | * 📢 Report issues or suggest improvements in the Issues tab |
