Pelagic Insight Research

73% of Inlet Coordinates
Were Wrong

GNIS — the federal standard for geographic coordinates — systematically points to ICW channel references instead of ocean entrances. We used satellite imagery and vision AI to find and fix it.

Eric Whyne · Data Machines · February 20, 2026

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95
coordinates corrected
10 min
per audit pass
~$5
compute cost
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The Systematic Bias

GNIS classifies inlets as “Channel” features — so coordinates point to the channel (ICW), not the ocean entrance where boats actually transit.

📍

GNIS Coordinate

Points to a spot on the Intracoastal Waterway — 0.3 to 8.5 nm from the actual ocean entrance.

Used by: NOAA, Coast Guard, chart plotters, every fishing app
🎯

Ocean Entrance

Where boats actually cross the bar, face breaking waves, and need precise depth data to survive.

Where coordinates should point for navigational safety

Largest Corrections Found

Biloxi, MS8.54 nm
Apalachicola, FL6.52 nm
Hilton Head, SC5.32 nm
Wassaw Sound, GA5.09 nm
St. Augustine, FL~1.7 nm

Four Iterations to the Solution

From manual discovery to automated verification — each approach built on the limitations of the last.

1

Manual Review

Limited

Developer notices Shallotte Inlet's coordinates are wrong. Effective but impractical at scale — 15-20 hours for 130 inlets.

2

GNIS Database Audit

Dead end

Systematic query confirms bias is widespread. But GNIS IS the ground truth — no alternative database to compare against.

3

Satellite Map Review

Effective but slow

Built /inlets page overlaying coordinates on Mapbox satellite imagery. Human can see the problem instantly — but still manual.

4

Vision AI Automation

Solution

Satellite imagery + vision AI audits all 130 inlets in 10 minutes. 95 corrections deployed to production.

Two-Pass Vision AI Methodology

Coarse screening with one model, pixel-precise refinement with another.

Pass 1: Grok-2-Vision

Inlets flagged125 / 130
Corrections deployed95
Processing time~10 min
ApproachFree-form lat/lon

Initial developer reaction: “A tool that flags 96% of inputs must be broken.” Manual verification proved: the AI was right.

Pass 2: Claude (Pixel-Based)

Inlets refined120
Skipped (ambiguous)2
Processing time~10 min
ApproachPixel → geo conversion

Key insight: vision models are better at spatial identification within an image than at geographic coordinate estimation from pixel positions.

💡 The Human Skepticism Bottleneck

The biggest barrier wasn't model accuracy — it was the team's reluctance to trust consistent findings that challenged assumptions about a federal database. When a tool built to detect a known bias detects that bias consistently, the appropriate response is verification, not dismissal.

Manual Review vs. Vision AI

Manual Review — Time for 130 inlets3–4 hours
Vision AI — Time for 130 inlets~10 min
Manual Review — CostAnalyst labor
Vision AI — Cost~$5–10
130
Inlets audited
Maine to Texas
95
Coordinates corrected
73% of all inlets
8.54 nm
Largest correction
Biloxi, MS
2
Ambiguous cases
Ossabaw Sound, Tampa Bay

Accurate Coordinates Save Lives

When a boater navigates to an inlet in deteriorating conditions, the difference between the ocean mouth and a point on the ICW is not academic — it's the difference between entering safely and searching in breaking seas.

1.

USGS should review and update GNIS coordinates for coastal inlets

2.

A verified ocean-entrance coordinate dataset should be published as open data

3.

Federal agencies should adopt vision AI as a routine QA tool for geographic databases

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