How Niantic is Using Pokémon Go to Build the Future of Geospatial 2.0

Following up on my recent talk, here’s a deeper dive into Niantic’s Large Geospatial Model and why it’s a game-changer for Geospatial 2.0.

Setting the Stage

Imagine you’re standing behind a historic church in a small European town. You’ve never been there before, but your brain immediately paints a picture of the front—its grand doors, stained glass windows, and towering spire. Now imagine a machine doing the same thing, understanding not just what’s in front of it but what lies beyond, even if it hasn’t been scanned. That’s the promise of Niantic’s Large Geospatial Model (LGM)—a revolutionary leap in spatial intelligence powered by data from Pokémon Go players and other Niantic apps.

Niantic, the company famous for turning our neighborhoods into Pokémon playgrounds, is transforming gaming data into a geospatial innovation that could redefine how we interact with the physical world. This isn’t just a clever tech trick; it’s a cornerstone of Geospatial 2.0, the era where location-based technologies and AI converge to solve real-world challenges.

From Games to Spatial Intelligence

When players of Pokémon Go or Ingress explore their surroundings, they’re not just chasing virtual creatures—they’re building one of the most advanced geospatial systems in the world. By scanning landmarks, streets, and parks, they’re feeding Niantic’s Visual Positioning System (VPS), which uses these scans to create incredibly detailed 3D maps of the environment. What makes this data unique is its pedestrian perspective, capturing areas vehicles can’t access, like narrow alleys, hiking trails, or hidden courtyards.

Over five years, Niantic has built a library of over 10 million scanned locations, with 1 million fresh scans added weekly. These maps don’t just capture geometry—they also understand the environment, recognizing trees, buildings, and other features. This forms the foundation for the LGM, which combines these localized models into a global understanding of the physical world.

What Makes the Large Geospatial Model Different?

To understand the LGM, let’s compare it to Large Language Models (LLMs) like ChatGPT. LLMs use text data to learn language and generate content, while LGMs use millions of images and scans to learn about physical spaces. Where LLMs answer questions like, “What is the capital of France?” an LGM answers, “Where exactly am I standing, and what does the world around me look like—even beyond what I can see?”

This shift from text to spatial understanding is transformative. While LLMs are revolutionizing how we interact with information, LGMs are poised to revolutionize how machines understand and navigate the world. It’s the next step in artificial intelligence, and it’s deeply tied to the evolution of Geospatial 2.0.

Why This Matters for Geospatial 2.0

The Large Geospatial Model isn’t just about maps—it’s about creating a bridge between the physical and digital worlds. Geospatial 2.0, the next phase of geospatial technology, is all about leveraging new data sources, advanced AI, and immersive platforms to solve complex problems. Here’s how the LGM fits into this vision:

1. Richer Data for Smarter Cities: Traditional maps are static snapshots. LGMs provide dynamic, 3D data that can help urban planners design smarter cities, optimize traffic flow, and create more livable spaces.
2. New Dimensions for AR: Augmented reality experiences, like Pokémon appearing in your local park, are just the beginning. The LGM enables persistent, shared AR interactions—where digital objects are tied to real-world locations and visible to everyone.
3. Autonomous Navigation: Robots and drones powered by LGMs can navigate complex environments like warehouses or hospitals with human-like understanding of spaces, reducing errors and improving efficiency.
4. Immersive Education and Tourism: Imagine walking through a historic site and having AR glasses provide a guided tour, placing historical events and characters right where they happened. The LGM makes this kind of spatial storytelling possible.

Challenges and Ethical Questions

While the potential of LGMs is enormous, it raises important questions. Many players who contributed data to Niantic’s systems likely didn’t know their scans would be used for advanced AI. This highlights the need for transparency around data use and ownership. Who benefits from this technology? And how do we ensure that contributors remain informed and respected?

The Future of Spatial Computing

The Large Geospatial Model represents a foundational shift in how we interact with technology. Where traditional geospatial tools have focused on mapping what’s already there, the LGM enables machines to predict, extrapolate, and enhance. This shift is central to Geospatial 2.0, which aims to unlock new opportunities by combining AI, immersive technology, and geospatial data in ways we’ve never seen before.

Niantic’s work shows how gaming, often seen as entertainment, can drive profound advancements in technology. The LGM is more than a tool for AR or robotics—it’s a blueprint for how AI will navigate and understand our world in the years to come. From creating smarter cities to powering the next generation of spatial computing devices, this is technology that will touch every corner of our lives.

As we move forward, the question isn’t just what the LGM can do, but how we, as individuals and industries, will leverage it to build a better, more connected world. This is Geospatial 2.0 in action, and the future is closer than we think.

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