""" Geolocation Engine Multi-layer geolocation using evidence from images """ from typing import Dict, List, Optional, Tuple from dataclasses import dataclass import numpy as np @dataclass class GeolocationEstimate: """Geolocation estimate with confidence""" lat: float lng: float accuracy: float # meters confidence: float # 0-1 method: str # gps, visual, semantic, geometric, temporal, ensemble evidence: Dict class GeolocationEngine: """ Multi-layer geolocation engine Methods: 1. GPS (if available) 2. Visual geolocation (landmarks, buildings) 3. Semantic geolocation (signs, text) 4. Geometric geolocation (vanishing points, camera pose) 5. Temporal geolocation (historical matches) 6. Map matching (OpenStreetMap, GIS) 7. Similarity matching (embeddings) 8. Ensemble fusion (combine all) """ def __init__(self): self.map_data = None # OpenStreetMap data self.historical_observations = [] # Previous observations self.embedding_index = None # FAISS or similar def geolocate(self, evidence_package) -> GeolocationEstimate: """ Estimate geolocation from evidence package Returns best estimate with confidence """ estimates = [] # Method 1: GPS (if available) if evidence_package.metadata.gps_lat and evidence_package.metadata.gps_lng: gps_estimate = self._gps_geolocate(evidence_package) if gps_estimate: estimates.append(gps_estimate) # Method 2: Visual geolocation visual_estimate = self._visual_geolocate(evidence_package) if visual_estimate: estimates.append(visual_estimate) # Method 3: Semantic geolocation semantic_estimate = self._semantic_geolocate(evidence_package) if semantic_estimate: estimates.append(semantic_estimate) # Method 4: Geometric geolocation geometric_estimate = self._geometric_geolocate(evidence_package) if geometric_estimate: estimates.append(geometric_estimate) # Method 5: Temporal geolocation temporal_estimate = self._temporal_geolocate(evidence_package) if temporal_estimate: estimates.append(temporal_estimate) # Method 6: Map matching map_estimate = self._map_match(evidence_package) if map_estimate: estimates.append(map_estimate) # Method 7: Similarity matching similarity_estimate = self._similarity_match(evidence_package) if similarity_estimate: estimates.append(similarity_estimate) # Method 8: Ensemble fusion if len(estimates) > 1: final_estimate = self._ensemble_fusion(estimates) elif len(estimates) == 1: final_estimate = estimates[0] else: # No estimates available final_estimate = GeolocationEstimate( lat=0.0, lng=0.0, accuracy=100000, # 100km confidence=0.0, method="none", evidence={"error": "No geolocation evidence available"} ) return final_estimate def _gps_geolocate(self, evidence) -> Optional[GeolocationEstimate]: """Geolocate using GPS metadata""" metadata = evidence.metadata if not metadata.gps_lat or not metadata.gps_lng: return None # GPS accuracy depends on device and conditions accuracy = 10.0 # Default 10 meters if metadata.altitude: accuracy = 5.0 # Better if altitude available return GeolocationEstimate( lat=metadata.gps_lat, lng=metadata.gps_lng, accuracy=accuracy, confidence=0.9, method="gps", evidence={ "gps_lat": metadata.gps_lat, "gps_lng": metadata.gps_lng, "altitude": metadata.altitude } ) def _visual_geolocate(self, evidence) -> Optional[GeolocationEstimate]: """Geolocate using visual landmarks""" # In production, match against landmark database # For now, return None if not evidence.visual_objects: return None # Check for known landmarks landmarks = { "eiffel_tower": (48.8584, 2.2945), "statue_of_liberty": (40.6892, -74.0445), "big_ben": (51.5007, -0.1246) } for obj in evidence.visual_objects: if obj.label in landmarks: lat, lng = landmarks[obj.label] return GeolocationEstimate( lat=lat, lng=lng, accuracy=50.0, confidence=obj.confidence * 0.8, method="visual", evidence={"landmark": obj.label, "confidence": obj.confidence} ) return None def _semantic_geolocate(self, evidence) -> Optional[GeolocationEstimate]: """Geolocate using semantic objects (signs, text)""" if not evidence.text_detections: return None # In production, geocode text addresses # For now, check for known locations in text location_keywords = { "bangkok": (13.7563, 100.5018), "tokyo": (35.6762, 139.6503), "london": (51.5074, -0.1278), "new york": (40.7128, -74.0060) } for text_det in evidence.text_detections: text_lower = text_det.text.lower() for location, coords in location_keywords.items(): if location in text_lower: return GeolocationEstimate( lat=coords[0], lng=coords[1], accuracy=1000.0, # 1km confidence=text_det.confidence * 0.6, method="semantic", evidence={"text": text_det.text, "matched_location": location} ) return None def _geometric_geolocate(self, evidence) -> Optional[GeolocationEstimate]: """Geolocate using geometric features""" # In production, use camera pose estimation # For now, return None return None def _temporal_geolocate(self, evidence) -> Optional[GeolocationEstimate]: """Geolocate using temporal signals""" # In production, match against historical observations # For now, return None return None def _map_match(self, evidence) -> Optional[GeolocationEstimate]: """Match against map data (OpenStreetMap)""" # In production, use OSM data # For now, return None return None def _similarity_match(self, evidence) -> Optional[GeolocationEstimate]: """Match against historical observations using embeddings""" # In production, use FAISS for similarity search # For now, return None return None def _ensemble_fusion(self, estimates: List[GeolocationEstimate]) -> GeolocationEstimate: """Combine multiple estimates using weighted average""" if not estimates: return None if len(estimates) == 1: return estimates[0] # Weight by confidence total_weight = sum(e.confidence for e in estimates) if total_weight == 0: # Equal weights if no confidence weights = [1.0 / len(estimates)] * len(estimates) else: weights = [e.confidence / total_weight for e in estimates] # Weighted average lat = sum(e.lat * w for e, w in zip(estimates, weights)) lng = sum(e.lng * w for e, w in zip(estimates, weights)) # Accuracy is weighted average accuracy = sum(e.accuracy * w for e, w in zip(estimates, weights)) # Confidence is max confidence confidence = max(e.confidence for e in estimates) # Method is ensemble methods = [e.method for e in estimates] return GeolocationEstimate( lat=lat, lng=lng, accuracy=accuracy, confidence=confidence, method=f"ensemble({','.join(methods)})", evidence={ "methods": methods, "estimates": [ {"lat": e.lat, "lng": e.lng, "accuracy": e.accuracy, "confidence": e.confidence, "method": e.method} for e in estimates ] } ) # Example usage def example_geolocation(): """Example: Geolocation estimation""" from evidence_extractor import EvidencePackage, ImageMetadata from datetime import datetime engine = GeolocationEngine() # Create sample evidence evidence = EvidencePackage( image_id="demo_001", timestamp=datetime.now(), metadata=ImageMetadata( gps_lat=13.7563, gps_lng=100.5018, altitude=10.0 ), visual_objects=[], semantic_objects=[], text_detections=[], geometric_features=[], environmental_signals=[], temporal_signals={} ) # Geolocate estimate = engine.geolocate(evidence) print(f"Geolocation estimate:") print(f" Lat: {estimate.lat}") print(f" Lng: {estimate.lng}") print(f" Accuracy: {estimate.accuracy}m") print(f" Confidence: {estimate.confidence}") print(f" Method: {estimate.method}") return estimate if __name__ == '__main__': example_geolocation()