163 lines
4 KiB
Markdown
163 lines
4 KiB
Markdown
# GIS Classification
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Python project for land cover classification using GIS data (GeoTIFF + Shapefile).
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## Project Structure
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```
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gis-classification/
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├── main.py # Main script with parameters
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├── requirements.txt # Dependencies
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├── Dockerfile # Docker image configuration
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├── docker-compose.yml # Docker Compose configuration
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├── .dockerignore # Docker ignore rules
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├── data/ # Input data folder
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├── output/ # Classification results
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├── static/ # Web frontend
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└── src/
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├── classifier.py # Main classification pipeline
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├── api.py # FastAPI web server
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├── data/
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│ └── loader.py # Data loading (GeoTIFF, Shapefile)
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├── strategies/
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│ ├── base.py # Strategy interface
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│ └── classifiers.py # Built-in strategies (RF, SVM, LR)
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└── utils/
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```
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## Installation
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```bash
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pip install -r requirements.txt
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```
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Or with uv:
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```bash
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uv pip install -r requirements.txt
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```
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### Docker Deployment
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**Build and run with Docker Compose:**
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```bash
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docker-compose up --build
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```
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**Or run with Docker directly:**
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```bash
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docker build -t gis-classification .
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docker run -p 8000:8000 -v $(pwd)/data:/app/data -v $(pwd)/output:/app/output gis-classification
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```
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Then open http://localhost:8000 in your browser.
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**Production with Nginx (optional):**
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1. Uncomment the nginx section in `docker-compose.yml`
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2. Create `nginx.conf` (see example below)
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3. Run `docker-compose up -d`
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Example `nginx.conf`:
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```nginx
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events { worker_connections 1024; }
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http {
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server {
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listen 80;
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location / {
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proxy_pass http://gis-classification:8000;
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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}
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}
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}
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```
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## Usage
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### CLI Mode
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1. Place your input files in `data/`:
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- `landsat.tif` - GeoTIFF from Landsat
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- `polygons.shp` - Shapefile with class labels
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2. Configure parameters in `main.py`:
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- Input/output paths
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- Classification strategy (RandomForest, SVM, LogisticRegression)
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- Training parameters
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3. Run:
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```bash
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python main.py
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```
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### Web Interface
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Start the web server:
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```bash
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uvicorn src.api:app --host 0.0.0.0 --port 8000 --reload
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```
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Then open http://localhost:8000 in your browser.
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**Features:**
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- Upload GeoTIFF raster and Shapefile training data
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- Select classification strategy (Random Forest, SVM, Logistic Regression, MLE)
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- View training metrics (Accuracy, Cohen's Kappa)
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- Interactive map visualization with Leaflet
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- Download classified GeoTIFF results
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**API Endpoints:**
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- `GET /` - Web interface
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- `POST /train` - Train classifier with uploaded files
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- `POST /predict` - Run classification
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- `GET /result/{session_id}` - Get result metadata
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- `GET /result/{session_id}/download` - Download classified GeoTIFF
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- `GET /docs` - Interactive API documentation (Swagger UI)
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## Adding Custom Classification Strategy
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Create a new class implementing `ClassificationStrategy`:
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```python
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from src.strategies import ClassificationStrategy
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import numpy as np
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class MyCustomStrategy(ClassificationStrategy):
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def train(self, X: np.ndarray, y: np.ndarray) -> None:
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# Your training logic
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pass
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def predict(self, X: np.ndarray) -> np.ndarray:
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# Your prediction logic
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pass
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def predict_proba(self, X: np.ndarray) -> np.ndarray:
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pass
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def get_params(self) -> dict:
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pass
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@property
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def name(self) -> str:
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return "MyCustom"
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```
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Then use in `main.py`:
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```python
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STRATEGY = MyCustomStrategy()
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```
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## Output
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- `output/classified.tif` - Classified raster (GeoTIFF)
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- Console output with accuracy metrics (Accuracy, Cohen's Kappa)
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- Web interface visualization with interactive map
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## Metrics
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The classifier reports:
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- **Accuracy**: Overall classification accuracy
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- **Cohen's Kappa**: Agreement statistic accounting for chance (values > 0.8 indicate excellent agreement)
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