Oil Spill Detection // Naval Forensic Oil Spill Attribution Console

SIH 2026 Problem Statement ID: 26143

Oil Spill Detection is an intelligence-grade, dual-mode (real-time live streaming & air-gapped offline fail-safe) maritime forensic platform that detects marine oil slicks on Sentinel-1 SAR imagery, backtracks their drift using 2D Lagrangian metocean numerical physics, and correlates spatio-temporal AIS vessel trajectories to identify and legally indict the polluting vessel.

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Visual Showcase

Authentic Radar dB View	Forensic Hollow Mask View
Sentinel-1 C-Band backscatter with pitch-black capillary damping void & ship corner reflectors	0% fill-opacity hollow outline exposing the pure satellite radar void underneath

Thermal Void Colormap	Polarimetric Dual-Pol False Color
Cryogenic violet slick against radiant amber/volcanic thermal clutter	Polarimetric VV/VH ratio composite with sea-green surface roughness

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1. Architecture & System Overview

1. ⚡ LIVE PRODUCTION MODE: Ingests real-time AIS transponder streams via WebSocket (wss://stream.aisstream.io/v0/stream) and fetches live hourly ERA5 winds & Copernicus Marine currents via Open-Meteo.
2. 🛡 OFFLINE FAIL-SAFE MODE: 100% self-contained and air-gapped with pre-cached verified datasets (for hackathons, field operations, and judging without internet). The system takes satellite SAR imagery (Sentinel-1 VV/VH), executes 3-class semantic segmentation (sea, look-alike, mineral oil) with a morphological descriptor engine, calculates 2D Lagrangian backward drift to identify the origin time and zone, reconstructs historical AIS vessel traffic, and computes a multi-factor explainable attribution score identifying the responsible vessel.

High-Level Data Flow:

[Sentinel-1 SAR GeoTIFF] 
       │
       ▼
[Module A: SAR U-Net & Morphological Engine] ──> GeoJSON (Centroid, Area, Perimeter, Orientation)
       │
       ▼
[Module B: 2D Lagrangian Physics & Metocean]  ──> Origin Time [T_orig], 90% Origin Polygon, Forecast Cone
       │
       ▼
[Module C: AIS Spatio-Temporal Join & Scoring] ──> Filtered Tracks, Dark Vessel Flags, Ranked Suspect Leaderboard
       │
       ▼
[Module D: FastAPI Backend & Leaflet Console]  ──> Full-Screen Tactical Map, Time Slider, PDF Dossier

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2. Feature Inventory

Every feature extracted during the Survey phase is enumerated below with its assigned milestone.

#	Feature	Description	Milestone	Status
F01	SAR Dual-Pol Tiling & Normalization	Ingest VV/VH bands, clip to $[-30, 0]$ and $[-35, -5]$ dB, tile to $512\times 512$ with overlap	M1	DONE
F02	3-Class Deep U-Net Segmentation	PyTorch U-Net (sea=0, look-alike=1, mineral oil=2) with weights oil_unet_best.pt (<80MB)	M1	DONE
F03	Heuristic Adaptive Fallback Detector	Dark-pixel threshold ($\sigma_0^{VV} &lt; -22\text{ dB}$, local $\Delta &gt; 4.5\text{ dB}$, area $\ge 500\text{ px}$)	M1	DONE
F04	Morphological Characterisation Engine	Local aeqd Area ($km^2$), Perimeter ($km$), Feret Length ($km$), Width, PCA Orientation ($\theta$), Centroid, Compactness	M1	DONE
F05	RFC 7946 GeoJSON Serializer	Standardized GeoJSON feature collection for slicks and look-alikes	M1	DONE
F06	2D Lagrangian Numerical Advection Engine	RK2 / Euler surface drift: $\vec{V} = \vec{U}{\text{curr}} + 0.03 \mathbf{R}(15^\circ)\vec{U}{\text{wind}}$ with Coriolis leeway	M2	DONE
F07	Backward Ensemble Origin Backtracking	Backward stepping ($dt=-3600\text{s}$), $N=50$ particles, turbulent noise ($\sigma_c=0.1, \sigma_w=1.0$), 90% origin envelope & origin time	M2	DONE
F08	Forward 36h Dispersion Forecast	Forward stepping ($dt=+3600\text{s}$) 24-48h, generating dispersion cone & threatened zones	M2	DONE
F09	Pre-cached Offline Metocean Store	NetCDF / JSON 4D grid store ($u_c, v_c, u_{10}, v_{10}$) with spatial bilinear & temporal linear interpolator	M2	DONE
F10	MarineCadastre SQLite Store & Indexing	Composite B-Tree index on (timestamp, lat, lon) and 16-field AIS schema	M3	DONE
F11	Spatio-Temporal Filtering & Trajectory Interpolation	Spatial filter $R \le 10\text{ km}$, time window $[T_{\text{orig}} \pm 3\text{h}]$, 1-min linear interpolation	M3	DONE
F12	Transponder Silence & Dark Vessel Anomaly	Detect gaps $\Delta t \ge 30\text{ min}$ with dead-reckoning near origin; behavioral flags	M3	DONE
F13	Multi-Factor Explainable Attribution Scoring	$S = 0.40 S_{\text{prox}} + 0.20 S_{\text{type}} + 0.15 S_{\text{traj}} + 0.25 S_{\text{beh}}$, reason codes, % confidence	M3	DONE
F14	Synthetic AIS Realistic Scenario Generator	Planted culprit MT OCEAN VOYAGER + distractors + background traffic for offline demo	M3	DONE
F15	FastAPI REST Backend Service	/api/health, /api/scenes, /api/detect, /api/drift, /api/attribute, /api/run, /api/jobs/{id}	M4	DONE
F16	Full-Screen Leaflet 1.9 Tactical Console	Dark theme, magenta slicks, yellow dashed look-alikes, cyan origin cone, blue forecast, AIS tracks with risk markers	M4	DONE
F17	Synchronized Time-Slider Scrubber	Scrub between origin time $T_{\text{orig}}$, satellite pass $T_{\text{sat}}$, and forecast $T_{\text{sat}} + 36\text{h}$	M4	DONE
F18	Suspect Leaderboard & Telemetry Inspector	Ranked table with score progress bars, reason breakdown, click-to-focus vessel, execution trace log	M4	DONE
F19	Law-Enforcement PDF Dossier Generator	ReportLab binary PDF generator with executive summary, maps, evidence table, attribution verdict	M4	DONE
F20	Kaggle GPU Remote Training Pipeline	scripts/train_kaggle.ipynb (P100/T4 training on Zenodo tiles, WCE+Dice loss, mIoU validation)	M5	DONE
F21	Hugging Face Hub Storage Sync	Model weights repository SidMI/TideTrace-weights, lean laptop local footprint (<100MB)	M5	DONE
F22	Pre-cached Demo Scenes & Metocean Fixtures	Real & verified sample scenes for Arabian Sea, Bay of Bengal, Gulf of Mexico	M5	DONE
F23	Offline End-to-End Test Suite	test_advection.py, test_geo_centroid.py, test_ais_gap.py, test_scoring.py, test_pipeline_offline.py	M6	DONE
F24	Adversarial Hardening & Forensic Audit	Verification against mock shortcuts, network isolation check, zero-slop attestation	M6	DONE

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3. Milestones

#	Name	Scope	Dependencies	Status	Key Outputs
M1	SAR Detection & Morphological Engine	F01, F02, F03, F04, F05 (app/ml/, app/geo/)	None	DONE	app/ml/, app/geo/, 13 tests passed
M2	Lagrangian Drift Physics & Metocean Engine	F06, F07, F08, F09 (app/drift/, app/metocean/)	None	DONE	app/drift/, app/metocean/, 16 tests passed
M3	AIS Spatio-Temporal Join & Attribution Engine	F10, F11, F12, F13, F14 (app/ais/, app/scoring/)	None	DONE	app/ais/, app/scoring/, 24 tests passed
M4	Naval Tactical Web Console & REST API	F15, F16, F17, F18, F19 (app/api/, backend/, backend/static/)	M1, M2, M3	DONE	app/api/, backend/static/, 11 tests passed
M5	Remote Training Pipeline, HF Sync & Demo Fixtures	F20, F21, F22 (scripts/, data/)	None	DONE	scripts/, data/, 6/6 fixture tests passed
M6	E2E Integration, Full Test Verification & Forensic Audit	F23, F24 (tests/, full pipeline verification)	M1, M2, M3, M4, M5	DONE	106/106 tests passed, CLEAN audit verdict

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4. Code Layout

c:\Users\Siddhant\TideTrace\
├── app/
│   ├── __init__.py
│   ├── config.py              # System parameters, physics constants, scoring weights
│   ├── ml/
│   │   ├── __init__.py
│   │   ├── model.py           # PyTorch U-Net architecture (<80MB parameter size)
│   │   ├── preprocess.py      # Sentinel-1 VV/VH dB conversion, normalization, tiling
│   │   ├── segmenter.py       # 3-class segmentation inference & threshold fallback
│   │   └── fallback.py        # Heuristic dark-pixel morphological detector
│   ├── geo/
│   │   ├── __init__.py
│   │   ├── georef.py          # Affine transform & pixel-to-WGS84 mapping
│   │   └── morphology.py      # Local AEQD area, perimeter, Feret, PCA orientation, centroid
│   ├── drift/
│   │   ├── __init__.py
│   │   ├── advection.py       # 2D surface advection: V = U_curr + 0.03*R(15)*U_wind
│   │   ├── hindcast.py        # Backward ensemble backtracking, 90% origin envelope
│   │   └── forecast.py        # Forward 36h dispersion cone & threatened zones
│   ├── metocean/
│   │   ├── __init__.py
│   │   ├── store.py           # Offline NetCDF/JSON grid store & loader
│   │   └── interpolator.py    # 4D spatio-temporal bilinear/linear interpolator
│   ├── ais/
│   │   ├── __init__.py
│   │   ├── store.py           # SQLite indexed storage (MarineCadastre schema)
│   │   ├── filter.py          # Spatio-temporal bounding box & radius filter
│   │   ├── trajectory.py      # 1-min track interpolation & closest point of approach (CPA)
│   │   └── anomaly.py         # Dark vessel gap detection (dt >= 30m) & speed anomalies
│   ├── scoring/
│   │   ├── __init__.py
│   │   ├── engine.py          # Multi-factor formula: S = 0.4*S_prox + 0.2*S_type + 0.15*S_traj + 0.25*S_beh
│   │   └── explain.py         # Human-readable legal reason codes & confidence %
│   └── api/
│       ├── __init__.py
│       ├── main.py            # FastAPI application & route aggregation
│       ├── routes.py          # REST endpoints (/api/scenes, /api/run, etc.)
│       └── report.py          # ReportLab law-enforcement PDF dossier generator
├── backend/
│   └── static/
│       ├── index.html         # Naval tactical dark HUD interface
│       ├── css/
│       │   └── style.css      # Dark tactical styling, high-contrast layers
│       ├── js/
│       │   ├── app.js         # State management & REST client
│       │   ├── map.js         # Leaflet map layers (slicks, origin, forecast, AIS tracks)
│       │   ├── timeline.js    # Synchronized time-slider scrubber
│       │   └── ui.js          # Leaderboard table, telemetry inspector, trace logs
│       └── vendor/
│           └── leaflet/       # Vendored Leaflet 1.9.4 CSS/JS/images (100% offline)
├── scripts/
│   ├── train_kaggle.ipynb     # Kaggle GPU training notebook
│   ├── sync_hf.py             # Hugging Face model weight downloader/uploader
│   ├── build_metocean_cache.py# Pre-cache metocean grids for demo scenes
│   ├── build_synthetic_ais.py # Generate deterministic synthetic AIS traffic
│   └── verify_fixtures.py    # Verification script for offline data fixtures
├── data/
│   ├── scenes/                # Sentinel-1 GeoTIFF demo scenes & metadata
│   ├── metocean/              # Pre-cached ocean current & wind grids (.json/.nc)
│   ├── ais/                   # SQLite database (tidetrace_ais.db)
│   └── models/                # Trained checkpoints (oil_unet_best.pt)
└── tests/
    ├── test_advection.py      # Numerical drift & Coriolis physics assertions
    ├── test_geo_centroid.py   # Polygon geometry & metric coordinate accuracy
    ├── test_ais_gap.py        # Transponder silence gap detection
    ├── test_scoring.py        # Multi-factor suspect attribution ranking
    ├── test_pipeline_offline.py# Isolated network end-to-end pipeline test
    ├── test_sar_morphology.py # SAR preprocessing & U-Net tests
    ├── test_metocean_drift.py # Metocean interpolation & ensemble dispersion tests
    ├── test_ais_scoring_m3.py # AIS storage & scoring engine tests
    ├── test_api_m4.py         # REST API endpoints & PDF generation tests
    ├── test_e2e_tiers.py      # Comprehensive 4-tier requirement & scenario tests
    └── test_challenger_stress.py # Adversarial edge cases & mathematical stress tests

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5. Quick Start Guide

Prerequisites

• Python 3.10+
• NVIDIA CUDA GPU recommended (automatic CPU fallback enabled)

Installation

# 1. Clone repository
git clone https://github.com/Sid-V5/TideTrace.git
cd TideTrace

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate  # On Linux/macOS: source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Optional: configure API keys (for live data mode)
cp .env.example .env

Launching the Tactical Web Console

python -m uvicorn app.api.main:app --host 127.0.0.1 --port 8000

Open your browser and navigate to: http://127.0.0.1:8000/

Running the Verification Test Suite

pytest -v

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6. REST API Endpoints

Method	Endpoint	Description
GET	/api/health	System health status, active CUDA/CPU device, loaded weights, indexed AIS fixes
GET	/api/scenes	Catalog of pre-cached satellite scenes (Mumbai High, Chennai, Gulf of Mexico, Clean Sea)
POST	/api/run	Execute full end-to-end attribution pipeline (accepts use_live_data: bool)
POST	/api/metocean/live-fetch	Fetch live hourly ERA5 wind and Copernicus currents via Open-Meteo
POST	/api/ais/live-stream	Ingest real-time AIS transponder stream via WebSocket
GET	/api/reports/{id}/pdf	Generate and stream formal MARPOL Annex I binary PDF prosecution dossier

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7. Benchmark Verification & Performance

• Automated Test Suite: 108 tests passing in 30.46s (100% pass rate).
• Segmentation Model: PyTorch 3-Class U-Net (oil_unet_best.pt, 3.35M parameters, 12.79 MB FP32).
  • Clean Sea IoU: 0.9882
  • Look-alike IoU: 0.7415
  • Mineral Oil IoU: 0.8124 (Baseline threshold: 0.4850)
  • Macro mIoU: 0.8474 (Baseline threshold: 0.4757)
• Forensic Attribution Validation:
  • scene_mumbai_001: Culprit tanker MT OCEAN VOYAGER ranked #1 with 89.5% confidence (identifying 52-min AIS blackout and 12.5 knot discharge band).
  • scene_chennai_002: Culprit tanker MT COROMANDEL GLORY ranked #1 with 95.9% confidence.
  • scene_clean_004: Clean sea true negative; verified 0 false positive attributions.

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8. License

Distributed under the MIT License.