fishing boats that go dark

Problem statement

Illegal, unreported, and unregulated (IUU) fishing accounts for an estimated 11–26 million tonnes of catch annually — $10–23.5 billion in value, and on the order of one-fifth of the global marine catch (Agnew et al. 2009). The primary detection tool is the Automatic Identification System (AIS), which broadcasts vessel position via satellite. But IUU vessels routinely disable AIS transponders to avoid detection ("going dark"), and satellite radar mapping shows that 72–76% of the world's industrial fishing vessels are not publicly tracked by AIS at all, with the untracked activity concentrated in South and Southeast Asia and Africa (Paolo et al. 2024). Detecting and identifying these "dark vessels" using satellite imagery or other remote sensing requires distinguishing fishing vessels from thousands of non-fishing contacts (cargo ships, pleasure craft, natural features) across vast ocean areas.

Why this matters

IUU fishing threatens food security for the 3.2 billion people who obtain at least 20 percent of their animal protein from aquatic foods (FAO 2024), drives species toward collapse (particularly in West African and Southeast Asian waters), funds organized crime networks, and undermines the economic viability of legal fishing operations. Developing countries are the most exposed: Agnew et al. found illegal fishing rates highest in the Eastern Central Atlantic, with total estimated catches off West Africa running 40% higher than reported. Enforcement agencies cannot inspect what they cannot see — and patrol-based monitoring covers only a small fraction of the ocean area where IUU fishing occurs.

What’s been tried and why it hasn’t worked

Global Fishing Watch uses AIS data combined with machine learning to classify vessel behavior (fishing vs. transiting), but this is useless for vessels with AIS disabled. Synthetic aperture radar (SAR) satellites can detect vessels in all weather and lighting conditions, but SAR images have limited resolution for vessel classification and revisit times of 1–6 days — during which a vessel can travel thousands of kilometers. Optical satellite imagery (Sentinel-2, commercial providers) provides better classification potential but is blocked by cloud cover (which obscures much of the ocean at any given time) and darkness. VIIRS nighttime lights data can detect vessels using fishing lights but misses vessels fishing during the day. Patrol vessel and aircraft surveillance covers tiny fractions of EEZ areas. The fundamental challenge is that no single sensor modality provides the temporal coverage, spatial resolution, and all-weather capability needed for reliable dark vessel detection.

What would unlock progress

Multi-sensor fusion systems that combine SAR detection (all-weather, day/night) with optical classification and AIS correlation to build persistent maritime domain awareness. Machine learning models trained on labeled SAR signatures of known vessel types could enable automated classification from radar data alone. Cubesat constellations providing higher revisit rates (hours rather than days) at lower cost would reduce the gap between detections. On the policy side, mandatory vessel monitoring systems (VMS) for all commercial fishing vessels — currently required only above vessel-length thresholds that vary by jurisdiction — would shrink the dark fleet.

Entry points for student teams

A team could build a multi-sensor vessel detection pipeline using freely available Sentinel-1 SAR and Sentinel-2 optical data (via Copernicus Open Access Hub), correlating detected vessels with AIS data (available from MarineTraffic or Global Fishing Watch) to identify "dark" contacts. The classification problem — distinguishing fishing vessels from other contacts in SAR imagery — is a tractable computer vision challenge. A policy-focused team could analyze the coverage gaps in current VMS mandates across jurisdictions and model the impact of extending requirements.