Software engineer creates classic SimCity-style map of NYC—and argues that AI will be good for creatives, actually

I enjoy a good traced ray, AI-upscaled texture, or 'hallucinated' frame as much as the next person, but I think pixel art remains an important part of any art team's tool kit. Pixel art offers far more than a nostalgic throwback appeal, capable of a level of expression that would give even the most photorealistic polygons a run for their money. So when I stumbled across the Isometric NYC city map project, I was at first properly stoked—and then deeply conflicted.

Isometric NYC is the one-man effort of Andy Coenen, attempting to render all of New York City in an isometric pixel art style distinctly reminiscent of the SimCity series. But rather than laboriously building this city pixel-by-pixel, Coenen has instead leveraged multiple AI agents to create this city map. The entire process required much more work than dashing off a handful of prompts, but you are perhaps beginning to understand my sense of conflict.

Coenen himself is a software engineer currently conducting AI research at Google DeepMind. He explains that the original idea for Isometric NYC was to use Google's "Nano Banana to generate a pixel art map from satellite imagery tile-by-tile," though Coenen ultimately used a number of AI agents, including "Claude Code, Gemini CLI, and Cursor." He also shares that he "wound up writing almost no code for this project."

He first set to work by 'whiteboxing' city geometry using 3D CityGML data. Unfortunately, his original plan didn't work out because "there was a bit too much inconsistency between the 'whitebox' geometry and the top-down satellite imagery, and Nano Banana was prone to too much hallucination in resolving these differences."

Moving to Google Maps 3D tiles API's more precisely aligned geometry and texturing, Coenen encountered further issues with Nano Banana. Being such a large generative AI model, Nano Banana struggled to output a stylistically consistent city, and ultimately proved both too slow and too pricey for the project's intended scope. So, Coenen elected to train something smaller and cheaper.

By feeding a Qwen/Image-Edit model "a training dataset of ~40 input/output pairs," Coenen began to get AI-generated city tiles closer to his preferred style in a fine-tuning process that took "~4 hours and cost ~12 bucks." Many more weeks of work followed to create software that would allow you to zoom in and out of each tile without completely hammering your hardware.

At a glance, the final result is pretty breathtaking—even that billionaire apartment building everyone hates, 432 Park Avenue, is represented here. The illusion falters when you zoom all the way in; there's a telltale, AI goopiness that immediately dispels any illusion of purposefully placed pixels.

Now, on the one hand, creating all of this by hand would have taken a lifetime of work for one person, and at that point, what would have to sustain you throughout such a project is pure love of the pixel art game. That's all without getting into whether you decide to stay on top of representing New York City's constant stream of changes, or simply choose to let every pixel art building be its own time capsule. Coenen's method has produced a somewhat accurate chunk of New York in an isometric-inspired style on a comparatively shorter timeline.

It's important to note, though, that Coenen isn't motivated by the business-brained efficiency mindset we've perhaps come to expect from creative applications of generative AI. He shares, "I spent a decade as an electronic musician, spending literally thousands of hours dragging little boxes around on a screen. [...] This isn't creative. It's just a slog."

He acknowledges that this 'slog' can also hone your creative instincts, but still feels AI could represent 'the end of drudgery' in creative fields. Though interestingly, he ends that train of thought thus: "If you can push a button and get content, then that content is a commodity. Its value is next to zero. Counterintuitively, that’s my biggest reason to be optimistic about AI and creativity. When hard parts become easy, the differentiator becomes love."

And to me, that sounds like the Google DeepMind AI researcher agreeing that love of the game is key to art and human creativity.