Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming

1. 1
   Perform Initial Coarse Localization

   Begin with a traditional cross-view geo-localization (CVGL) method. Use it to obtain an initial, broad estimate of the location (latitude, longitude) and a corresponding coarse zoom level (e.g., zoom 15-16). This serves as the starting point for iterative refinement.
2. 2
   Generate Multi-Scale Overhead Context

   Based on the current estimated location, programmatically fetch or render a set of overlapping overhead image patches. For the next iteration, generate these patches at a *finer* zoom level (e.g., zoom 17-18) to progressively 'zoom in' on the area of interest. Ensure sufficient coverage for the next search.
3. 3
   Extract Embeddings for Matching

   Utilize your pre-trained contrastive embedding model. Extract feature vectors (embeddings) from the input street-view image and from each of the newly generated multi-scale overhead image patches. The model should be robust to minor scale and perspective variations within a local context.
4. 4
   Match, Refine, and Iterate

   Compare the street-view image embedding with the embeddings of all overhead patches to find the highest similarity score. Identify the best-matching overhead patch or region. Update your estimated location based on this match. Repeat steps 2-4, iteratively zooming in and refining the location until a desired precision is achieved or a maximum number of iterations is reached.

import numpy as np

def autoregressive_geo_localize(street_view_image, initial_lat, initial_lon, embedding_model, map_api_client, max_iterations=5, min_zoom=15, max_zoom=20):
    current_lat, current_lon = initial_lat, initial_lon
    current_zoom = min_zoom

    for i in range(max_iterations):
        print(f"Iteration {i+1}: Current estimate ({current_lat:.4f}, {current_lon:.4f}) at zoom {current_zoom}")

        # Step 2: Generate Multi-Scale Overhead Context
        overhead_patches = map_api_client.get_overhead_patches(current_lat, current_lon, current_zoom + i) # Dynamic zoom
        if not overhead_patches: break

        # Step 3: Extract Embeddings
        street_view_embedding = embedding_model.predict(street_view_image)
        overhead_embeddings = [embedding_model.predict(patch) for patch in overhead_patches]

        # Step 4: Match and Refine
        best_match_score = -1
        best_match_patch_idx = -1
        for idx, ov_emb in enumerate(overhead_embeddings):
            similarity = np.dot(street_view_embedding, ov_emb) / (np.linalg.norm(street_view_embedding) * np.linalg.norm(ov_emb))
            if similarity > best_match_score:
                best_match_score = similarity
                best_match_patch_idx = idx
        
        if best_match_patch_idx != -1:
            # Simulate updating location based on best match
            # In a real scenario, this would involve precise coordinate mapping from the matched patch
            current_lat += np.random.uniform(-0.001, 0.001) / (i+1) # Simulate refinement
            current_lon += np.random.uniform(-0.001, 0.001) / (i+1)
        else:
            print("No match found, stopping refinement.")
            break
        
        if current_zoom < max_zoom: current_zoom += 1 # Increase zoom level for next iteration

    return current_lat, current_lon

# Example usage (requires actual models and map client)
# class MockEmbeddingModel:
#     def predict(self, image): return np.random.rand(128)
# class MockMapAPIClient:
#     def get_overhead_patches(self, lat, lon, zoom): return [f"patch_{zoom}_{i}" for i in range(5)]

# mock_embedding_model = MockEmbeddingModel()
# mock_map_client = MockMapAPIClient()
# mock_street_view_image = "street_view_data"
# final_lat, final_lon = autoregressive_geo_localize(mock_street_view_image, 34.0522, -118.2437, mock_embedding_model, mock_map_client)
# print(f"Final estimated location: ({final_lat:.6f}, {final_lon:.6f})")