GeoSight MCP Server

""" Land Cover Classification using Deep Learning. """ import asyncio from pathlib import Path from typing import Tuple, Optional, Literal import numpy as np import structlog from geosight.config import settings logger = structlog.get_logger(__name__) LAND_COVER_CLASSES = { 0: {"name": "Forest", "color": "#228B22"}, 1: {"name": "Shrubland", "color": "#90EE90"}, 2: {"name": "Grassland", "color": "#ADFF2F"}, 3: {"name": "Cropland", "color": "#FFD700"}, 4: {"name": "Built-up", "color": "#DC143C"}, 5: {"name": "Barren", "color": "#DEB887"}, 6: {"name": "Water", "color": "#4169E1"}, 7: {"name": "Wetland", "color": "#20B2AA"}, } class LandCoverClassifier: """Deep learning model for land cover classification.""" def __init__( self, model_name: Literal["eurosat", "deepglobe", "custom"] = "eurosat", device: Optional[str] = None, ): self.model_name = model_name self.device = device or settings.model.inference_device self._model = None self._initialized = False async def classify( self, imagery: np.ndarray, ) -> Tuple[np.ndarray, np.ndarray]: """ Classify land cover in satellite imagery. Returns: Tuple of (classification_map, confidence_map) """ # For demo, generate synthetic classification return self._generate_demo_classification(imagery.shape[:2]) def _generate_demo_classification( self, shape: Tuple[int, int], ) -> Tuple[np.ndarray, np.ndarray]: """Generate demo classification.""" height, width = shape np.random.seed(42) x = np.linspace(0, 4, width) y = np.linspace(0, 4, height) xx, yy = np.meshgrid(x, y) noise = np.sin(xx) * np.cos(yy) + 0.5 * np.sin(2*xx + yy) noise = (noise - noise.min()) / (noise.max() - noise.min()) class_map = (noise * 7).astype(np.int32) confidence_map = 0.7 + 0.3 * np.random.random(shape).astype(np.float32) return class_map, confidence_map