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

src/data/loader.py

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+"""Data loading module for GIS classification."""
+
+from dataclasses import dataclass
+from typing import Optional
+import rasterio
+import geopandas as gpd
+import numpy as np
+from rasterio.mask import mask
+
+
+@dataclass
+class RasterData:
+    """Container for raster data."""
+
+    array: np.ndarray
+    transform: rasterio.transform.Affine
+    crs: rasterio.crs.CRS
+    nodata: Optional[float] = None
+    bands: int = 1
+
+
+@dataclass
+class VectorData:
+    """Container for vector data."""
+
+    gdf: gpd.GeoDataFrame
+    class_column: str
+
+
+def load_raster(path: str) -> RasterData:
+    """Load GeoTIFF raster file.
+
+    Args:
+        path: Path to the GeoTIFF file.
+
+    Returns:
+        RasterData object with array, transform, CRS and metadata.
+    """
+    with rasterio.open(path) as src:
+        # Read all bands
+        array = src.read()
+        transform = src.transform
+        crs = src.crs
+        nodata = src.nodata
+
+    return RasterData(
+        array=array,
+        transform=transform,
+        crs=crs,
+        nodata=nodata,
+        bands=array.shape[0] if len(array.shape) == 3 else 1,
+    )
+
+
+def load_vector(path: str, class_column: str = "class") -> VectorData:
+    """Load Shapefile vector data.
+
+    Args:
+        path: Path to the Shapefile.
+        class_column: Name of the column containing class labels.
+
+    Returns:
+        VectorData object with GeoDataFrame and class column name.
+
+    Raises:
+        ValueError: If class_column doesn't exist in the Shapefile.
+    """
+    gdf = gpd.read_file(path)
+
+    if class_column not in gdf.columns:
+        raise ValueError(
+            f"Column '{class_column}' not found. Available columns: {list(gdf.columns)}"
+        )
+
+    return VectorData(gdf=gdf, class_column=class_column)
+
+
+def extract_raster_values_by_polygons(
+    raster_path: str, vector: VectorData
+) -> tuple[np.ndarray, np.ndarray]:
+    """Extract raster values for each polygon in the vector layer.
+
+    Args:
+        raster_path: Path to the GeoTIFF raster file.
+        vector: VectorData object.
+
+    Returns:
+        Tuple of (features, labels) arrays for classification.
+        features: 2D array of shape (n_samples, n_bands)
+        labels: 1D array of shape (n_samples,)
+    """
+    features_list = []
+    labels_list = []
+
+    with rasterio.open(raster_path) as src:
+        nodata = src.nodata
+
+        for idx, row in vector.gdf.iterrows():
+            geometry = row[vector.gdf.geometry.name]
+            label = row[vector.class_column]
+
+            # Mask raster by polygon geometry
+            try:
+                masked_data, _ = mask(src, [geometry], crop=True)
+            except Exception as e:
+                print(f"Skipping polygon {idx}: {e}")
+                continue
+
+            # Get valid pixels (not nodata)
+            if nodata is not None:
+                valid_mask = masked_data != nodata
+            else:
+                valid_mask = np.ones_like(masked_data, dtype=bool)
+
+            # Extract valid pixel values for all bands
+            if len(masked_data.shape) == 3:
+                # Multi-band: reshape to (bands, pixels)
+                band_values = masked_data[:, valid_mask[0]]
+            else:
+                # Single band
+                band_values = masked_data[valid_mask]
+
+            if band_values.size > 0:
+                # Transpose to (pixels, bands)
+                if len(band_values.shape) == 1:
+                    band_values = band_values.reshape(-1, 1)
+                else:
+                    band_values = band_values.T
+
+                features_list.append(band_values)
+                labels_list.append(np.full(band_values.shape[0], label))
+
+    if not features_list:
+        raise ValueError("No valid pixels extracted from polygons")
+
+    features = np.vstack(features_list)
+    labels = np.concatenate(labels_list)
+
+    return features, labels