Automation Workflow for Illegal, Unreported, and Unregulated (IUU) Fishing Detection

Manual patrols are a massive, fixed-cost line item with low probability of interdiction. A predictive workflow focuses expensive air and sea assets on high-probability targets identified by correlating satellite radar (SAR) with Vessel Monitoring System (VMS) and AIS data gaps. Savings come from optimizing fuel, crew time, and vessel maintenance against confirmed risk, not random patrol grids.

Analysts manually cross-referencing satellite feeds with AIS logs to find 'dark vessels' is a slow, error-prone process. An automated pipeline ingests multi-source data, runs gap-detection algorithms, and surfaces suspected IUU vessels with a risk score and supporting imagery in a dashboard. This compresses the detection-to-alert cycle from 48+ hours to under 10 minutes, enabling rapid response before vessels leave the enforcement zone.

Human teams can only monitor a fraction of a nation's Exclusive Economic Zone (EEZ). An automated, always-on workflow processes terabytes of satellite data daily, providing persistent surveillance across millions of square kilometers. This creates a continuous, auditable intelligence layer, uncovering patterns of evasion (e.g., transshipment at sea, loitering in closed seasons) that were previously invisible.

Court cases often fail due to weak evidence chains. A governed workflow automatically packages timestamped satellite imagery, AIS/VMS discrepancies, and vessel track history into a forensically sound case file. This defensible digital evidence, generated by a traceable system, strengthens legal standing and increases the likelihood of successful fines, license revocations, or vessel seizures.

IUU fishing directly steals revenue from licensed fishers and depletes fish stocks, threatening long-term economic and food security. By automating detection and enforcement, fisheries management authorities can protect quota systems, ensure stock sustainability, and secure legitimate tax and license fees. The ROI is measured in preserved GDP, increased legal catch value, and compliance with international treaties.

Reactive enforcement is politically volatile. A predictive system allows authorities to diplomatically confront foreign-flagged vessels before they enter protected zones, using intelligence as leverage. Automated alerts can be shared with regional fisheries management organizations (RFMOs) to coordinate sanctions, creating a powerful deterrent effect that reduces infringements over time.

This agent orchestrates the continuous ingestion and normalization of disparate data streams: Synthetic Aperture Radar (SAR) imagery from satellites like Sentinel-1 for all-weather vessel detection, terrestrial and satellite AIS for vessel identity and course, and proprietary VMS data from flag states. It handles API polling, data lake writes, and timestamp synchronization, creating a unified event stream for downstream analysis. Data quality checks and gap detection for missing feeds are built-in.

The core detection logic compares SAR-derived vessel positions against reported AIS/VMS positions within a spatiotemporal buffer. Vessels present in SAR but absent from transponder feeds within a protected zone or during a closed season are flagged as 'dark targets.' This engine uses probabilistic matching and accounts for AIS latency and reception gaps. It outputs a preliminary event with confidence scoring and associated metadata (location, time, estimated vessel size from SAR backscatter).

For each dark vessel event, this agent enriches the alert with risk context. It queries historical tracks for loitering patterns near marine protected areas, analyzes vessel size against known IUU profiles, and checks proximity to port infrastructure used for transshipment. A composite risk score (e.g., High/Medium/Low) is generated using a configurable rules engine, allowing enforcement agencies to triage alerts based on local priorities and available patrol assets.

This component formats the enriched alert into structured reports (PDF, GeoJSON) and pushes them via API into enforcement and operations systems. Integrations can include fisheries management platforms (e.g., OceanMind's Neptune), coast guard command-and-control (C2) systems like Palantir, or vessel monitoring dashboards. It supports configurable approval gates for human-in-the-loop validation before dissemination and maintains a full audit trail of all alerts and actions.

The entire workflow is coordinated by a LangGraph-based orchestrator that defines the state machine for event processing, handles conditional branching (e.g., 'if confidence score > 0.8, skip human review'), and manages retries for failed API calls. A dedicated observability agent logs every step, tracks performance metrics (detection rates, latency), and surfaces operational health to a dashboard. This layer ensures the workflow is debuggable, maintainable, and adaptable to new data sources or detection rules.

To maintain accuracy, this automated pipeline collects ground-truth outcomes from enforcement actions (e.g., 'patrol confirmed illegal fishing') and uses them to retrain the SAR vessel detection and behavioral risk models. It manages versioning, A/B testing in a staging environment, and controlled promotion to production. This closed-loop system ensures the workflow adapts to evolving IUU tactics and improves over time without manual data science intervention.

Responsible for building and maintaining the data ingestion pipeline that fuses VMS, AIS, and satellite SAR/optical feeds. This role ensures low-latency, normalized data streams for the detection models, managing integrations with providers like Spire, Planet, and ESA's Copernicus. They architect the data lake and implement quality checks to flag sensor outages or corrupt data before it impacts analysis.

Owns the development, deployment, and continuous retraining of the core computer vision and anomaly detection models. This includes the SAR vessel detection algorithm, the AIS/VMS correlation logic for identifying 'dark vessels,' and the risk-scoring agent. They implement model monitoring for drift, manage the CI/CD pipeline in tools like MLflow or Kubeflow, and coordinate with data engineers on feature store updates.

The primary operational user and domain expert. They define the detection rules (e.g., closed season dates, marine protected area boundaries, vessel size thresholds) within the system's orchestration layer. This role reviews high-risk alerts, provides false-positive feedback to improve models, and authorizes the escalation of confirmed cases to enforcement. They rely on the workflow's dashboard for situational awareness and reporting.

Receives actionable, prioritized alerts from the system via integrated platforms (e.g., C4ISR, Vessel Traffic Service software). This role translates technical alerts into operational patrol orders, coordinating asset dispatch. They provide critical feedback on interception outcomes, which is fed back into the system to close the loop on alert accuracy and improve patrol resource allocation models.

Designs and implements the workflow orchestration using frameworks like LangGraph or Temporal, ensuring reliable execution from detection to alerting. This role builds the APIs and connectors to downstream systems such as national fisheries management databases, coast guard command centers, and evidence logging systems. They are responsible for system observability, logging, and the rollback procedures for any deployment.

Ensures the automated workflow's outputs meet evidentiary standards for prosecution and adhere to data sovereignty and privacy regulations. This role establishes the approval gates and audit trails within the system, defines the chain-of-custody for digital evidence, and validates that the risk-scoring logic does not introduce biased enforcement. They are critical for regulatory buy-in and the system's long-term operational legitimacy.