Pokémon Go Powers Niantic’s New Large Geospatial AI Model

Niantic, the creator of the world-renowned AR) game Pokémon Go, has announced its most ambitious project to date: a "Large Geospatial Model" (LGM). Leveraging years of user-contributed data, Niantic aims to bridge the gap between the digital and physical worlds, creating AI systems capable of perceiving, understanding, and interacting with real-world spaces in unprecedented ways.

What is the Large Geospatial Model?

The LGM is a groundbreaking AI system that builds on Niantic’s existing Visual Positioning System (VPS). Over the past five years, the VPS has relied on millions of location scans from players of Pokémon Go, Ingress, and Scaniverse to generate detailed 3D maps. This geospatial data, collected from a pedestrian’s perspective, provides a unique and rich dataset that cars or drones cannot access. Today, Niantic has over 10 million scanned locations globally, with approximately one million new scans added weekly, each comprising hundreds of discrete images.

Niantic likens the LGM to Large Language Models (LLMs), such as OpenAI’s GPT, which process vast amounts of text data to generate human-like language. Similarly, the LGM uses billions of geotagged images to create a unified understanding of physical spaces. It doesn’t just map locations but also extrapolates unseen details by drawing upon shared characteristics of similar structures worldwide. For example, if a local model has only scanned the front of a building, the LGM can predict what the back might look like based on its knowledge of thousands of similar buildings.

Core Applications of the LGM

While initially developed to enhance games like Pokémon Go, the LGM has applications far beyond gaming. Niantic envisions this technology as a foundational component for spatial computing, which integrates digital and physical realities. The LGM’s potential use cases include:

• Augmented Reality Wearables: The LGM will significantly enhance AR glasses, enabling users to experience real-world navigation, guided tours, and interactive digital overlays with centimeter-level precision.

• Robotics: By training robots to navigate human-centric spaces, the LGM could revolutionize industries like logistics, food delivery, and even healthcare.

• Content Creation: Game developers and digital artists could use the LGM to design immersive, real-world-compatible experiences, from virtual tours to interactive urban environments.

• Autonomous Systems: The LGM offers a pedestrian-focused perspective that complements existing datasets for autonomous vehicles and drones.

One early demonstration of this technology is the Pokémon Playgrounds feature in Pokémon Go, which allows players to place virtual Pokémon in real-world locations. These Pokémon remain anchored to the spot, enabling other players to interact with them even after the original user has left. This is a glimpse of what the LGM can achieve in blending physical and virtual spaces.

How the LGM Works

At its core, the LGM uses machine learning to build a global, unified model from localized neural maps. Unlike traditional 3D models that rely on individual data points, the LGM encodes spatial information into neural networks, enabling it to extrapolate missing data. For example, even if a local map has only captured part of a location, the LGM can predict unscanned details by referencing patterns observed in similar locations worldwide.

This capability mirrors how humans process spatial understanding. Imagine walking through a European town square for the first time—you can visualize the unseen parts of buildings or navigate back to a location even after seeing it from just one angle. The LGM aspires to replicate this human-like spatial reasoning, making it a revolutionary step forward for AI.

The Future of Spatial Computing

Niantic believes that spatial intelligence will underpin the next generation of technology, much like operating systems power today’s devices. The company envisions LGMs transforming urban planning, logistics, remote collaboration, and audience engagement. For wearable AR devices, this technology could offer real-time navigation, personalized recommendations, and cultural insights, all tailored to the user’s surroundings.

Niantic’s long-term goal is to create an interconnected ecosystem where various foundational models—LLMs, multimodal models, and LGMs—work together. For example, a system combining language understanding with spatial intelligence could answer questions like, “Where can I find the nearest historical landmark?” while simultaneously guiding the user there with AR overlays.

Ethical and Privacy Considerations

Niantic’s announcement has sparked discussion about the ethical implications of using crowdsourced data. Many Pokémon Go players were unaware that their gameplay data would one day contribute to an AI model. While this practice isn’t uncommon in the tech industry, it raises questions about user consent and the commercial use of personal data. Niantic has yet to clarify its policies on how this data will be used or shared, particularly in sensitive applications like robotics.

Promising Technology

Niantic’s Large Geospatial Model represents a monumental leap in AI and AR capabilities, turning casual gameplay into the foundation for a global spatial computing system. By blending real-world data with advanced machine learning, Niantic is not only redefining augmented reality but also setting the stage for a new era of digital-physical integration.

As this technology evolves, it promises to unlock a multitude of possibilities across industries, from gaming to urban planning and robotics. However, as the LGM moves forward, Niantic—and the industry at large—must address the ethical and privacy concerns associated with this transformative technology. Only time will tell how this fusion of gameplay and geospatial AI will reshape our understanding of the world.