GeoSight MCP Server

""" Change Detection using Siamese Networks. """ from typing import Tuple, Optional import numpy as np import structlog from geosight.config import settings logger = structlog.get_logger(__name__) class ChangeDetector: """Siamese network for bi-temporal change detection.""" def __init__(self, device: Optional[str] = None): self.device = device or settings.model.inference_device self._model = None async def detect( self, before_image: np.ndarray, after_image: np.ndarray, threshold: float = 0.3, ) -> Tuple[np.ndarray, np.ndarray]: """ Detect changes between two images. Returns: Tuple of (change_mask, change_magnitude) """ return self._compute_change(before_image, after_image, threshold) def _compute_change( self, before: np.ndarray, after: np.ndarray, threshold: float, ) -> Tuple[np.ndarray, np.ndarray]: """Compute change using image differencing.""" # Ensure same shape if before.shape != after.shape: min_h = min(before.shape[0], after.shape[0]) min_w = min(before.shape[1], after.shape[1]) before = before[:min_h, :min_w] after = after[:min_h, :min_w] # Handle multi-band images if before.ndim == 3: before = np.mean(before, axis=2) if after.ndim == 3: after = np.mean(after, axis=2) # Normalize before = (before - before.mean()) / (before.std() + 1e-8) after = (after - after.mean()) / (after.std() + 1e-8) # Compute difference diff = np.abs(after - before) # Normalize to 0-1 diff = (diff - diff.min()) / (diff.max() - diff.min() + 1e-8) # Create binary mask change_mask = (diff > threshold).astype(np.uint8) return change_mask, diff.astype(np.float32)