| --- |
| title: EcoPulse |
| emoji: 🌿 |
| colorFrom: green |
| colorTo: green |
| sdk: streamlit |
| sdk_version: "1.28.0" |
| python_version: "3.12" |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # EcoPulse: Advanced Satellite Vegetation Analysis |
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| > **EcoPulse** is a high-performance deep learning pipeline designed for the automated detection, segmentation, and quantification of vegetation in satellite imagery. |
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| By integrating Meta's **Segment Anything Model (SAM)** with custom-trained convolutional neural networks (CNNs), EcoPulse provides a scalable, instance-aware alternative to traditional multispectral vegetation indices. |
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| --- |
|
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| ## 🌟 Core Capabilities |
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|
| | Capability | Description | |
| | :--- | :--- | |
| | **Instance-Aware Segmentation** | Utilizes SAM (ViT-B) for zero-shot segmentation of satellite imagery, identifying distinct land-cover objects with pixel-level precision instead of relying on raw spectral ratios. | |
| | **Multi-Class Classification** | A fine-tuned ResNet-50 classifier (trained on EuroSAT) categorizes segmented masks into 10 distinct land-cover classes, differentiating between natural forests, pasture land, and urban greenery. | |
| | **Quantitative Auditing** | Aggregates classification results to compute the exact Greenery Coverage Percentage of a given region—essential for urban planning and environmental compliance. | |
| | **Explainable AI (XAI)** | Features Grad-CAM visualization for class activation heatmaps and supports side-by-side region comparison to contrast environmental health metrics transparently. | |
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| --- |
|
|
| ## 📊 Technical Results |
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| The pipeline has been rigorously validated against industry-standard datasets, demonstrating robust morphological feature extraction: |
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| | Metric / Analysis | Result | Context | |
| | :--- | :--- | :--- | |
| | **mIoU** | `0.3356` | Segmentation performance on DeepGlobe Land Cover dataset. | |
| | **Dice Coefficient** | `0.5020` | Segmentation performance on DeepGlobe Land Cover dataset. | |
| | **Vegetation Correlation**| `Pearson = -0.9094` | Strong negative correlation with NGRDI, validating learned morphological features against traditional spectral indices. | |
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| --- |
|
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| ## 🚀 Getting Started |
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| ### Prerequisites |
| - **Python:** 3.12+ (managed automatically via `uv`) |
| - **Hardware:** NVIDIA GPU with CUDA support (Recommended) |
| - **Tooling:** [uv](https://github.com/astral-sh/uv) (Highly Recommended) |
|
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| ### Installation |
| For comprehensive details, please refer to the [Installation Guide](docs/INSTALLATION.md). |
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| **Quick start with `uv`:** |
| ```bash |
| uv sync |
| uv run streamlit run app.py |
| ``` |
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| --- |
|
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| ## 💻 Usage |
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| ### Interactive Dashboard (Recommended) |
| Launch the Streamlit GUI to perform real-time analysis and region comparisons: |
| ```bash |
| streamlit run app.py |
| ``` |
|
|
| ### Command Line Interface |
| Execute various components of the pipeline directly from the terminal: |
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|
| | Action | Command | |
| | :--- | :--- | |
| | **Evaluation Pipeline** | `python scripts/evaluate_segmentation.py` | |
| | **Interpretability Analysis** | `python scripts/evaluate_interpretability.py` | |
| | **Region Comparison** | `python scripts/compare_regions.py --image_a path/to/area1.jpg --image_b path/to/area2.jpg` | |
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| --- |
|
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| ## 📂 Project Structure |
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| | Directory / File | Description | |
| | :--- | :--- | |
| | `src/` | Core logic (CNN models, SAM utilities, greenery estimation). | |
| | `scripts/` | Production scripts for training, evaluation, and comparison. | |
| | `app.py` | Interactive Streamlit dashboard entry point. | |
| | `config/` | Centralized configuration management. | |
| | `docs/` | Technical documentation and architecture deep-dives. | |
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| --- |
|
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| ## 📄 License |
| This project is licensed under the MIT License. See `LICENSE` for details. |
