GIS Classification

Python project for land cover classification using GIS data (GeoTIFF + Shapefile).

Project Structure

gis-classification/
├── main.py              # Main script with parameters
├── requirements.txt     # Dependencies
├── data/                # Input data folder
├── output/              # Classification results
├── static/              # Web frontend
└── src/
    ├── classifier.py    # Main classification pipeline
    ├── api.py           # FastAPI web server
    ├── data/
    │   └── loader.py    # Data loading (GeoTIFF, Shapefile)
    ├── strategies/
    │   ├── base.py          # Strategy interface
    │   └── classifiers.py   # Built-in strategies (RF, SVM, LR)
    └── utils/

Installation

pip install -r requirements.txt

Or with uv:

uv pip install -r requirements.txt

Usage

CLI Mode

1. Place your input files in data/:

   • landsat.tif - GeoTIFF from Landsat
   • polygons.shp - Shapefile with class labels

2. Configure parameters in main.py:

   • Input/output paths
   • Classification strategy (RandomForest, SVM, LogisticRegression)
   • Training parameters

3. Run:

python main.py

Web Interface

Start the web server:

uvicorn src.api:app --host 0.0.0.0 --port 8000 --reload

Then open http://localhost:8000 in your browser.

Features:

• Upload GeoTIFF raster and Shapefile training data
• Select classification strategy (Random Forest, SVM, Logistic Regression, MLE)
• View training metrics (Accuracy, Cohen's Kappa)
• Interactive map visualization with Leaflet
• Download classified GeoTIFF results

API Endpoints:

• GET / - Web interface
• POST /train - Train classifier with uploaded files
• POST /predict - Run classification
• GET /result/{session_id} - Get result metadata
• GET /result/{session_id}/download - Download classified GeoTIFF
• GET /docs - Interactive API documentation (Swagger UI)

Adding Custom Classification Strategy

Create a new class implementing ClassificationStrategy:

from src.strategies import ClassificationStrategy
import numpy as np

class MyCustomStrategy(ClassificationStrategy):
    def train(self, X: np.ndarray, y: np.ndarray) -> None:
        # Your training logic
        pass

    def predict(self, X: np.ndarray) -> np.ndarray:
        # Your prediction logic
        pass

    def predict_proba(self, X: np.ndarray) -> np.ndarray:
        pass

    def get_params(self) -> dict:
        pass

    @property
    def name(self) -> str:
        return "MyCustom"

Then use in main.py:

STRATEGY = MyCustomStrategy()

Output

• output/classified.tif - Classified raster (GeoTIFF)
• Console output with accuracy metrics (Accuracy, Cohen's Kappa)
• Web interface visualization with interactive map

Metrics

The classifier reports:

• Accuracy: Overall classification accuracy
• Cohen's Kappa: Agreement statistic accounting for chance (values > 0.8 indicate excellent agreement)