Developer onboarding guide to DVSA-API (https://github.com/ravibeta/dvsa-api)

 

Core components to harden for onboarding:

1.      Web API and pipeline entrypoints The apps/videos and agent_kits modules are the primary entrypoints. They should be documented as “developer surfaces”:

a.      REST endpoints for video upload, analysis runs, and analytics queries (e.g., detections per video, per mission).

b.      Agent kits (local_interactive, cloud_autonomous, orchestration) as runnable examples: “run this pipeline on a sample video,” “run unattended in the cloud,” “partition and merge large runs.”

Spec: stabilize and document:

c.      VideoUploadAPIView contract (request/response JSON, supported storage backends).

d.      RunAnalysisView contract (which routines, which adapters, how to pass parameters).

e.      A canonical run_pipeline signature and its expected inputs/outputs.

2.      Analytics routines and model adapters The apps.analytics routines and custom_model ONNX adapter are your equivalent of ADE’s “schemas and extraction logic.”

Spec:

a.      Define a canonical detection schema (id, label, bbox, confidence, world_coord, source, model_version, mission, project).

b.      Document how to register a new routine in apps.analytics.routines and how to plug in a new adapter (e.g., Triton, Ray Serve, Rekognition, Azure Vision).

c.      Provide at least one sample custom model (ONNX) with labels and a ready‑to‑run routine (custom_onnx_detection) that uses real aerial imagery.

3.      Storage adapters and data layout The storage adapters already support Azure/S3/local. For onboarding, you want a standard layout for drone video and frames:

a.      Video objects (original uploads) in videos/ or raw/.

b.      Extracted frames in frames/ with predictable naming (video_id/frame_id.jpg).

c.      Detections in a structured store (DB + optional S3 JSON manifests).

Spec:

d.      Document the storage layout and how to configure it via .env (AZURE_*, S3 settings, local paths).

e.      Provide a sample storage_config.yaml or .env snippet for each backend.

4.      Observability and run introspection You already have an observability app; onboarding needs developer‑visible run introspection:

a.      Per‑run logs and metrics (frames processed, detections per label, latency).

b.      Simple UI or API to fetch “run summary” for a video.

Spec:

c.      Define a RunSummary API: given video_id, return counts, latency stats, and links to detections.

d.      Add a minimal HTML or JSON view that developers can hit in a browser or via curl.

Developer‑facing “Gallery” for drone video with a Drone Sensing Gallery that lives in docs/gallery.md and a small static front‑end:

Examples (each with a sample video, screenshots, and a short description):

·        “Detect vehicles in high‑resolution aerial imagery”

·        “Track moving objects across frames and compute trajectories”

·        “Detect construction equipment and safety violations on job sites”

·        “Detect crop health anomalies using NDVI or RGB imagery”

·        “Detect people and vehicles near restricted zones (geofenced alerts)”

·        “Handle shaky drone footage and variable frame rates”

·        “Handle mixed resolutions and camera poses (different drones)”

Spec:

·        For each gallery item, provide:

o   A sample video (or short clip) in infra/demo/videos/.

o   A JSON manifest of detections.

o   A short narrative: what the pipeline does, which routines/adapters are used, and how to run it (agent_kits command or API call).

·        Add a simple static page (Django template or React/Vue) that lists these examples with links to run them locally.

Sample projects layout (ADE‑style) where ADE organizes sample projects into Workflows, Use_Cases, Events inside dvsa‑api (or as a sibling repo):

·        sample_projects/Workflows/

o   basic_ingest_and_detect/ — minimal pipeline: upload video, extract frames, run a detector, store detections, query via API.

o   batch_analysis_with_celery/ — show Celery‑driven large video analysis.

o   cloud_autonomous_runner/ — show FastAPI cloud runner processing videos from a queue.

·        sample_projects/Use_Cases/

o   traffic_monitoring/ — detect vehicles, compute counts per road segment.

o   construction_site_safety/ — detect people vs equipment, flag proximity violations.

o   agriculture_scouting/ — detect crop stress regions.

·        sample_projects/Events/

o   Hackathon or conference demos (e.g., “Drone Safety Demo 2026”).

·        sample_projects/Other/

o   Utilities: frame extractor scripts, dataset converters, labeling helpers.

Each sample project should have:

·        Its own README.md with setup and usage.

·        A requirements.txt or pointer to dvsa‑api’s requirements/base.txt.

·        A small test suite (pytest) that validates the pipeline on a tiny sample video.

Developer quickstart (drone video version) with existing local dev setup in the README.md.

Spec for a Drone Video Quickstart:

·        Step 0: clone dvsa‑api, create venv, install requirements/base.txt, configure .env (DB, Celery, storage).

·        Step 1: run Django + Celery (python manage.py runserver, celery -A config worker -l info).

·        Step 2: run agent_kits.local_interactive.cli_wrapper with a provided sample video:

Bash:

python -m agent_kits.local_interactive.cli_wrapper run \
  --video file:///path/to/sample_drone_video.json \
  --routine custom_onnx_detection \
  --dry-run

·        Step 3: run a non‑dry pipeline and then query detections via REST:

Bash:

curl -s http://localhost:8000/api/analytics/videos/1/detections/ | jq .

·        Step 4: open the Gallery page and see the run visualized.

The quickstart should be a single docs/quickstart_drone_video.md plus a demo.sh that automates most of it.

Contracts and schemas to make dvsa‑api “plug‑and‑play” for developers with specific JSON contracts:

·        Video upload request/response.

·        Frame representation (id, timestamp, s3_uri or local path, camera_pose, sensor_meta).

·        Detection representation (as above).

·        Mission/project metadata (tags, geofences, sampling policies).

Spec:

·        Add docs/contracts/video.json, frame.json, detection.json, mission.json.

·        Ensure apps/videos and apps/analytics endpoints return these canonical shapes.

·        Document how agent_kits expect video manifests (e.g., JSON describing video URI, frame extraction settings, mission metadata).

Front‑end and visualization for dvsa-api add to the existing dvsa-ui (https://github.com/ravibeta/dvsa-ui) the following:

·        A simple web UI that:

o   Lists videos and their analysis status.

o   Shows detections overlaid on frames (bounding boxes, labels, confidence).

o   Shows geospatial overlays (world_coord on a map) for trajectories.

Spec:

·        Add a minimal front‑end under apps/frontend or frontend/:

o   Use Django templates or a small SPA (React/Vue).

o   Consume existing REST endpoints (videos, analytics/detections).

·        Provide a “Gallery” view that links to each sample project and shows screenshots.

Tests, CI, and reproducibility to make dvsa‑api safe to extend:

·        Expand tests/ to include:

o   Pipeline tests for each sample project (run on tiny videos).

o   Contract tests for REST endpoints (JSON shapes).

o   Adapter tests (ONNX, cloud AI, Rekognition) with mocks.

·        CI:

o   Run pytest -q on every PR.

o   Optionally run a small end‑to‑end demo (sample video) in CI with mocked adapters.

Spec:

·        Add tests/sample_projects/ with one test per sample.

·        Add scripts/run_demo_pipeline.sh that CI can call.

Infra and deployment templates with exisitng infra/terraform and docker‑compose.

Spec:

·        Provide a infra/demo/drone_sensing_stack/:

o   docker‑compose for Django, DB, Celery, storage (local or S3 emulator).

o   Optional OTel/Honeycomb observability if you keep that spec.

·        Provide Terraform or Bicep templates for cloud deployment (Azure, AWS) with:

o   dvsa‑api app service / ECS.

o   Storage (blob/S3).

o   Queue/broker (Redis, RabbitMQ).

Implementation checklist (high‑level) roadmap:

·        Week 1–2:

o   Define and document canonical contracts (video, frame, detection, mission).

o   Harden apps/videos and apps/analytics endpoints to use them.

o   Add one end‑to‑end sample project (basic_ingest_and_detect).

·        Week 3–4:

o   Build Drone Sensing Gallery (docs/gallery.md + simple UI).

o   Add 2–3 use‑case sample projects (traffic, construction, agriculture).

o   Add quickstart doc and demo.sh.

·        Week 5–6:

o   Expand tests and CI for sample projects.

o   Add infra demo stack and cloud deployment templates.

o   Polish docs (docs/integrations, docs/contracts, docs/gallery).

References: previous article: https://1drv.ms/w/c/d609fb70e39b65c8/IQBLgFai-AMCSJvSvzTXi5sXAd-RcY1cWwzO9iImLLwr9RU?e=GNbf9c

 Geographical grounding tests:

Question 1:

Where is the scene in this image:

https://sadronevideo.blob.core.windows.net/input/interesting/what-location.jpg?sp=r&st=2026-08-16T01:11:16Z&se=2026-08-16T09:26:16Z&spr=https&sv=2026-02-06&sr=b&sig=oQNQKuNfUIF%2FBQV%2F%2Bz%2Fl1fmvwAZQwwb%2BOknG1s05%2BR8%3D

Answer: Google Search for Harvard Hillel aerial drone image for GPS co-ordinates (frame15.jpg)

The circular courtyard building visible at the center of the image is Rosovsky Hall, which houses Harvard Hillel. It is located on Mount Auburn Street in Cambridge, Massachusetts. [1]

The closest GPS coordinates for this specific building are:

• 42°22'17"N, 71°07'09"W (or in decimal format: 42.3714, -71.1192)

Question 2:

Could you explain your thinking in how you try to answer the question: "what location is this image in terms of nearest GPS co-ordinates"?

 

To find the nearest GPS coordinates for an overhead drone or satellite image like this, the process follows a structured, step-by-step logic that combines visual analysis, text parsing, and geographical indexing.

Here is exactly how that thinking breaks down:

1. Identify Distinctive Visual Anchors (The "Fingerprints")

First, look for unique infrastructure, natural markers, or geometric shapes that narrow down the map. In this image, the key markers include:

• 

• The Dual Railway Line: A straight, heavily weathered double-track railroad running vertically through the center.

• Building Architecture: A gray, industrial warehouse or depot with a prominent rectangular parking lot directly to its north, marked with distinctive white parking paint grids.

• Residential Layout: To the left of the tracks, a series of repetitive, rectangular multi-story apartment blocks separated by manicured green spaces and pathways.

• Industrial Zone: To the right, a denser industrial park with rows of specialized buildings (like the sawtooth-style roof shapes on the far right).

• 

2. Isolate Regional Clues

Next, assess cultural and environmental factors to determine the general region or country:

• 

• Tree Foliage: The autumn-colored yellow and orange trees mixed with green lawns suggest a temperate climate, likely in Europe or North America.

• Automobiles & Lanes: The narrow roads, vehicle types, and compact parking stalls heavily favor European urban layouts rather than North American ones.

• 

3. Cross-Reference via Reverse Image Search

Using advanced visual indexing tools, the overall image layout is cross-referenced against global satellite databases and image registries.

For instance, this exact overhead perspective matches stock drone footage tracking railway traffic in an urban area. Advanced reverse searches or visual matching often point directly to specific urban project sites, industrial developments, or localized geographic portfolios.

4. Zeroing In via Local Mapping Data

Once a lead points to a specific city or district (for example, industrial corridors in central or eastern European cities), matching the exact spatial layout confirms the final location:

• 

• Align the angle of the railroad tracks.

• Count the exact number of apartment buildings on the left to verify the pattern.

• Locate the specific warehouse roof shape and parking lot layout.

• 

5. Pinpointing the Coordinates

Once the exact building is identified on a dynamic mapping service, placing a digital pin at the geometric center of that industrial building provides the precise latitude and longitude.

 

The image is located at the BMW Group Plant Berlin in the locality of Spandau, Berlin, Germany. [1] 

Coordinates and Visual Verification

• 

• Exact Coordinates: 52.5447, 13.2268

• The Railway Line: The dual tracks running through the middle belong to the Berlin-Spandau ship canal railway corridor / regional train line running adjacent to the plant.

• The Multi-Story Blocks: The buildings seen to the left of the tracks are part of the residential housing complex along Am Juliusturm and Melli-Beese-Straße.

• The Warehouse and Parking Layout: The gray industrial structure with the gridded parking spaces in the center right belongs specifically to the manufacturing and logistics grounds of the BMW Motorrad motorcycle facility. [1] 

[1] https://www.allaboutlean.com

[2]https://commons.wikimedia.org/wiki/File:Luftbild_der_HALLE_Tor_2_und_der_Wassermannhalle_in_K%C3%B6ln,_Deutschland.jpg

[3] Enhancing ezbenchmark with these tests: https://1drv.ms/w/c/d609fb70e39b65c8/IQB-2twykvMKT7J4lm_MDHt9AY_KjN_nI-YGHomlgldVEWM?e=f7QIOT