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a74054f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | """
Land use feature extractor.
Single Responsibility: Extract land cover and vegetation features.
"""
import geopandas as gpd
from typing import Dict, Any
from .base import BaseFeatureExtractor
class LandUseExtractor(BaseFeatureExtractor):
"""
Extracts land use and vegetation features.
Features:
- Forest cover (%)
- Agricultural land (%)
- Urban area (%)
- NDVI or vegetation index
- Impervious surface (%)
"""
def extract(self, basin_gdf: gpd.GeoDataFrame) -> Dict[str, Any]:
"""
Extract land use features from basin.
Args:
basin_gdf: GeoDataFrame with basin geometry
Returns:
Dictionary with land use features
"""
if not self.validate_inputs(basin_gdf):
raise ValueError("Invalid basin GeoDataFrame")
# Placeholder for land use extraction
# Would involve:
# - Rasterizing land use/land cover datasets (Corine, ESA WorldCover)
# - Computing zonal statistics per class
# - Extracting NDVI from satellite imagery (Sentinel-2, Landsat)
return {
"forest_cover_pct": None,
"agricultural_land_pct": None,
"urban_area_pct": None,
"water_body_pct": None,
"ndvi_mean": None,
"ndvi_std": None,
"impervious_surface_pct": None
}
def get_feature_names(self) -> list:
"""Get list of feature names."""
return [
"forest_cover_pct", "agricultural_land_pct",
"urban_area_pct", "water_body_pct",
"ndvi_mean", "ndvi_std", "impervious_surface_pct"
]
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