Offloading location services from https://github.com/ravibeta/dvsa-api

Geographical information about the scene in a selected aerial drone image comes very handy for analysis but is often missing or required to be determined accurately. While DVSA-API has a built-in functionality, this article explains what it is, how it can be done, and how it can be offloaded to independent vendors, services, or providers.

Using advanced visual indexing tools, the overall image layout is cross-referenced against global satellite databases and image registries. At a high level, advanced visual indexing tools do three things: they turn images into numeric fingerprints, they organize those fingerprints in a searchable index, and they tie every fingerprint to ground truth metadata like GPS coordinates, map tiles, and semantic labels. Modern systems use deep models to produce embeddings that capture layout, texture, and semantics (roads, rail lines, building footprints, vegetation) rather than just low-level features. Those embeddings are stored in vector indexes (FAISS, ScaNN, or cloud vector DBs) so that a new query image can be matched against millions of satellite or aerial tiles in milliseconds

For geolocation specifically, the index is built from georeferenced imagery: satellite platforms (Google Earth Engine, Sentinel Hub, Microsoft Planetary Computer), commercial providers (Planet, Maxar), and map services (Bing Maps, Mapbox, Google Maps) all expose imagery that is already aligned to latitude/longitude. Each tile gets an embedding plus metadata: coordinates, zoom level, capture time, and sometimes derived features like road graphs or land-use classes. When you send an overhead drone frame into the system, the model produces an embedding, the index returns the nearest neighbors, and you inherit their coordinates as candidate locations

OSINT geolocation workflows are essentially the manual version of this: analysts visually match road layouts, building shapes, rivers, and terrain against Google Earth, Bing, Yandex, etc., sometimes aided by tools like SunCalc for shadow-based latitude estimation. Automated systems encode those same cues—road topology, block patterns, coastline shapes, rail corridors—into embeddings and then run large-scale nearest-neighbor search instead of human eyeballing. The best practice in that world is to combine automated predictions with manual verification when confidence is low or stakes are high.

Extension of the DVSA-API for location determination requires:

1. Treat geolocation as an analytics routine. Add a geolocation_from_imagery routine in apps.analytics.routines that operates on keyframes or representative overhead frames. It should:

o Select frames that are likely to be georeferenceable (overhead, enough context, not just close-up objects).

o Normalize them (crop to a canonical aspect ratio, resize, maybe mask overlays).

2. Use a visual embedding model tuned for overhead imagery. Options:

o Self-host an open model trained on satellite/aerial data (e.g., a CLIP variant or remote-sensing model) and wrap it in your custom_model ONNX adapter so it fits your existing create_detector / adapter pattern.

o Or call a cloud service that exposes embeddings or similarity search over geospatial imagery (e.g., Azure AI Vision combined with Azure Maps/Planetary Computer, or a custom index you build on top of Sentinel Hub tiles).

3. Build or leverage a georeferenced index:

o Custom: periodically fetch tiles from open imagery sources (Sentinel, Planetary Computer, Google Earth Engine exports), tile them at a fixed zoom, compute embeddings offline, and store them in a FAISS index plus a relational/GeoPandas layer that holds coordinates and bounding boxes.

o Service-based: use APIs that already expose search over imagery or map tiles; you send an image or features, they return candidate locations or tiles. Some OSINT-oriented platforms and geocoding services are starting to offer this kind of functionality, though often behind commercial licenses.

4. Integrate with your pipeline adapters:

o In dvsa_api reasoning adapters, define a GeolocationAdapter that takes a frame URI, calls either your local FAISS index or a cloud service, and returns:

 Candidate coordinates (lat, lon)

 Confidence scores

 Optional supporting tile IDs or URLs for verification

o Wire this into run_pipeline so that when location metadata is missing from the input video, the pipeline can attempt geolocation as a secondary step and attach results to the video’s metadata.

5. Store and expose results:

o Use geopandas/shapely (already in your stack) to represent candidate locations as points or polygons and persist them via your videos app models.

o Add fields like inferred_location, inferred_location_confidence, and maybe inferred_location_candidates to your video or analysis result models.

o Expose them through your REST API and agent_kits so downstream consumers (or human analysts) can see and validate the geolocation.

6. Add reverse geocoding and human-in-the-loop:

o Once you have coordinates, call a reverse geocoding service (Azure Maps, OpenStreetMap Nominatim, etc.) to turn them into human-readable place names.

o For low-confidence cases, surface the top N candidate tiles and coordinates via your ChatAPIView or a dedicated endpoint so a human can confirm or correct the location. That correction can be logged and later used to refine your index or fine-tune the embedding model.

Sample:

def geolocate_frame(frame_uri, geolocation_adapter):

    embedding = geolocation_adapter.compute_embedding(frame_uri)

    candidates = geolocation_adapter.search_index(embedding, top_k=5)

    # candidates: list of {lat, lon, confidence, tile_id}

    best = max(candidates, key=lambda c: c["confidence"])

    return {

        "inferred_location": (best["lat"], best["lon"]),

        "confidence": best["confidence"],

        "candidates": candidates,

    }

Then your Celery-driven video analysis flow can call this for selected frames and attach the result to the video’s analysis record.