Setting the Scene¶
Maritime Domain Awareness: A Problem Worth Solving
The Houston Ship Channel is the busiest waterway in the United States. Over 280 million tons of cargo move through it every year, carried by more than 8,000 vessel transits per month through a channel that narrows to 530 feet in places. Tankers, container ships, barges, tugs, and offshore supply vessels share the waterway around the clock. At any given moment, a Coast Guard Vessel Traffic Service (VTS) watchstander at VTS Houston-Galveston is responsible for knowing what is out there, what the conditions are, and whether anything needs attention.
The information exists. Vessel positions come from AIS transponders, fed into the MTM300 radar and tracking system on the VTS watch floor. Weather alerts come from NOAA. Water levels come from a separate NOAA tides system. Navigational hazards (wrecks, light outages, pipeline operations, rig positions) come from NGA broadcast warnings. But none of these feed into MTM300. A VTS watchstander monitoring the Houston Ship Channel has to pull from all of these sources, across separate systems, to build a single operational picture.
The NTSB studied this problem. They examined six accidents in VTS-monitored waterways and found a pattern: in every case, conditions called for VTS action, but "action was not taken or need for action was not recognized" as situations developed. The most common factor was that developing risk was not detected in time. The NTSB concluded that VTS should take a more proactive role in traffic management, not just monitor but actively surface situations that need attention. Better tooling is part of closing that gap.
Today, you start building that tooling. Your team is going to build a Vessel Traffic Dashboard: a single-screen operational picture for a Coast Guard VTS watchstander. What vessels are in the area. What ports can service them. What the weather is doing. What the water levels are at channel stations. What navigational hazards are active nearby. Whether any traffic management measures are in effect. One view, one tool, built from real government data sources.
The Houston Ship Channel moves $927 billion in economic activity annually. When the next storm rolls in, when fog closes the channel, when a light goes out on a range marker near a shipping lane, the watchstander's job is to know about it before it becomes a crisis. That is what domain awareness means. That is what you are building.
How the Sprints Work¶
Your team will go through two challenge sprints. Each sprint builds on the last. You never start over. Here is how they work:
- Your team builds one thing together. One project, one demo at the end. Each person can experiment and explore different approaches, then come back together as a team, compare what you found, and combine the best parts.
- Sprint 2 builds on Sprint 1. What you create now carries forward. You will add new capabilities, integrate what you learn in Lesson 2, and push toward a more complete product.
- Baseline capabilities and stretch goals. Every sprint has a set of baseline capabilities everyone should aim for, plus stretch goals for teams that get there and want to push further.
- The goal is learning, not finishing. Understanding what you built and why it works matters more than checking every box. Help your teammates. Talk through decisions. Celebrate the wins together.
The Most Important Thing¶
You are about to use AI to build something real. Here is the one thing that will make the biggest difference in what you learn today:
Build one piece at a time.
The temptation will be to write out everything you want in one massive prompt and let AI handle it all at once. Don't. That is not how you learn this skill.
If you paste a wall of requirements and accept whatever comes back, you will have output, but you will not understand it. You will not know what worked, what didn't, or how to fix it. You will not develop the judgment for when AI nails it and when it needs a nudge. And that judgment is the whole point.
The value is in the back-and-forth. Write a user story. Send it. Look at what comes back. Does it match your acceptance criteria? If not, tell your AI tool exactly what to change. That cycle (prompt, evaluate, refine) is the skill that transfers to everything you do with AI after today.
Build incrementally. Verify as you go. Discuss as a team.