My journey into professional AI development didn't start with a complex algorithm. It started with a cup of coffee and a simple observation.

My first paid job as an AI consultant came from an engineering firm with a modern ambition: they wanted to "use AI" to make their processes more efficient. They knew the what, but they had no idea about the how.

Before I wrote a single line of code, my first task was to listen. I spent time with their team, shadowing their workflow, and asking questions. It was a process of discovery, not prescription. I quickly realized they were wasting an enormous amount of time on a tedious, manual, and mind-numbing task.

The problem was map-making.

A team of skilled technicians would be given a target neighborhood. Their job was to identify and plot the location of every single transmission and telephone pole in that area.

To do this, they would manually open Google Maps, drop a pin, and virtually "walk" the streets, block by block, using Street View. Every time they saw a pole, they'd place a marker.

A single map for a small neighborhood could take three hours. It was a colossal bottleneck, a drain on morale, and a misuse of skilled human capital.

I knew immediately that this was a perfect opportunity for automation. The mission was clear: teach a computer how to do this job. The solution seemed simple on the surface - image recognition. But the reality, as always, was far more complex.

This wasn't just a computer vision problem. It was a land navigation problem. And solving it would require me to reach back into the most unexpected part of my toolkit.

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Fusing Two Worlds: The Marine and the Coder

Before I was a developer, I was a Marine Corps Officer. In the Marines, they teach you how to navigate terrain with a map, a compass, and a protractor. You learn how to plot coordinates, shoot cross-sections to find your own position, and measure distance using the pace count you've memorized. It's a disciplined, mathematical, and deeply spatial skill.

Now, years later, I had to translate those physical principles into Python.

My plan was to build a system that would mimic the human process, but with the speed and scale of a machine:

Plot the Route: Take a set of waypoints from a Google Map and programmatically calculate the bearings and distances between them, generating a high-resolution path for the "walk."

Automate the Walk: "Travel" along this calculated path by systematically calling the Google

Street View API to capture images every few meters.

See and Identify: Feed each image into a custom-trained computer vision model to detect the presence of a utility pole.

Pinpoint the Location: This was the hardest part. When the model "saw" a pole, I had to use the camera's angle, its focal length, and the object's size in the image to perform a trigonometric calculation - a digital cross-section - to estimate the real-world GPS coordinates of that pole.

It took me six weeks of intense, focused development. Six weeks of fusing two completely different parts of my brain - the Marine's spatial awareness and the self-taught coder's problem-solving.

The Code Tells the Story

The project evolved from messy scripts into a sophisticated pipeline. The early code shows me wrestling with Selenium to literally screenshot pages before graduating to a more elegant API-based approach. The heart of the final application was a Python script that was part-navigator, part-robot, and part-analyst.

It would ingest the raw map data, clean it, and then begin its journey. For every waypoint, it would perform a geodesic calculation to plot its next step. At each step, it would call the Street View API, download the image, and pass it to a custom-trained transformers object detection model I had fine-tuned specifically for identifying utility poles.

When it got a positive hit, the real magic happened. A cascade of calculations would fire, converting pixel dimensions and camera headings into a predicted latitude and longitude.

The Result: A 80% Time Savings

The final demonstration was one of the most satisfying moments of my career. We took a mapping task that had taken a technician three hours that same morning. We fed the starting and ending points into my application.

Less than ten minutes later, it produced a CSV file containing the predicted GPS coordinates of every pole it had found along the route.

The process wasn't entirely human-free. The final step was a 20-minute review where a human would quickly scan the plotted points on a map, verify their accuracy, and make any minor adjustments.

The final tally: a 3-hour (180-minute) manual process was transformed into a 30-minute,
AI-assisted one. It was a 80% reduction in time.

The story has one final, validating postscript. As I was moving on to the next phase of development - planning to use adversarial training to improve the model's accuracy - the company was acquired. During the valuation process, the software I had built was recognized as a tangible company asset. My code had added to their bottom line.

It was my first paid project, and it taught me the most valuable lesson of my career: the most powerful solutions are born when you fuse your unique, real-world human experience with the limitless potential of technology. You don't just build code; you translate your past into the future.