feat: initial release of GIS classification project with strategy-based classifiers selector

README.md

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+# 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
+└── src/
+    ├── classifier.py    # Main classification pipeline
+    ├── data/
+    │   └── loader.py    # Data loading (GeoTIFF, Shapefile)
+    ├── strategies/
+    │   ├── base.py          # Strategy interface
+    │   └── classifiers.py   # Built-in strategies (RF, SVM, LR)
+    └── utils/
+```
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+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:
+```bash
+python main.py
+```
+
+## Adding Custom Classification Strategy
+
+Create a new class implementing `ClassificationStrategy`:
+
+```python
+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`:
+```python
+STRATEGY = MyCustomStrategy()
+```
+
+## Output
+
+- `output/classified.tif` - Classified raster (GeoTIFF)
+- Console output with accuracy metrics