Where You Operate Matters—Large Geospatial Models Make It Smarter

Reimagining the World Through Spatial AI and Large Geospatial Models

Large Geospatial Models (LGMs) are changing how organizations understand and act on location data. By applying advanced machine learning to massive geographic datasets, LGMs can analyze, interpret, and predict spatial patterns with unprecedented scale and precision. These models don’t just make maps smarter—they unlock a deeper understanding of the world, enabling entirely new ways to optimize operations, anticipate changes, and connect digital experiences with the physical environment.

Unlike traditional Geographic Information Systems (GIS), which are excellent for visualization and querying known data, LGMs actively learn from spatial relationships, adapt to new environments, and generate predictions without needing explicit instructions for each scenario. Traditional GIS requires analysts to know what they’re looking for. LGMs reveal patterns even when the question hasn't been asked yet.

At the heart of LGMs is the ability to improve localization—understanding where things are and how they relate to their surroundings. This enables more accurate decision-making in areas like infrastructure planning, market expansion, emergency response, and supply chain optimization.

As the company behind Pokémon GO and the world's most detailed pedestrian-level 3D map, Niantic Spatial is uniquely positioned to help enterprises leverage LGMs for transformative outcomes. With digital and physical spaces becoming increasingly intertwined, LGMs give business leaders a competitive edge by turning location data into actionable insights.

The Evolution of Geospatial Technology

The journey from paper maps to intelligent spatial models highlights how businesses have advanced their use of location data.

From Static Maps to Interactive GIS

Two decades ago, maps were static and required experts to interpret. The advent of GIS in the early 2000s introduced layering and basic spatial analysis. In 2005, Google Earth revolutionized public access to geographic data, transforming digital maps into an everyday utility—but primarily for visualization.

The Rise of Real-Time Spatial Computing

With the proliferation of GPS-enabled devices and cloud computing, companies began ingesting real-time location data and acting on it instantly. Logistics firms rerouted fleets based on live traffic, retailers monitored foot traffic in real time, and first responders collaborated through live maps of unfolding events.

Cloud platforms enabled scalable analysis across global datasets, removing previous storage and compute limitations. Enterprises could now fuse satellite imagery, IoT data, and business metrics into unified operational systems.

The Emergence of Large Geospatial Models: Enter Niantic Spatial

These advances paved the way for Large Geospatial Models—systems that apply AI to spatial problems at scale. Niantic’s pivotal moment came with Pokémon GO in 2016, which showcased the power of real-world augmentation layered over a detailed global map. Today, the same core technology powers Visual Positioning System (VPS), allowing for centimeter-level accuracy in millions of locations.

LGMs build on this by unifying local neural maps into a global model that recognizes patterns, understands environments, and generalizes knowledge to unseen locations. They don’t just position devices—they comprehend places.

These models allow businesses to:

• Forecast infrastructure needs before they arise

• Model traffic or crowd flows for urban planning

• Optimize field operations with environment-specific context

• Predict environmental changes and operational risks

Technical Foundations of Large Geospatial Models

Core Architectures and Technologies

LGMs rely on a layered architecture:

• Data ingestion from imagery, sensors, and behavioral data

• Processing layers that use AI to identify patterns and relationships

• Visualization layers for intuitive decision-making

• API access to integrate insights into enterprise systems

They are cloud-native by design—able to scale up for planetary datasets or down to local operations.

Visual Positioning System (VPS)

VPS extends geolocation beyond GPS by matching camera images to a reference database of neural maps. It enables reliable positioning in GPS-challenged environments like city centers, indoors, and forests. This is critical for use cases like AR, autonomous systems, and high-fidelity mapping.

Data Sources

LGMs pull from a wide range of inputs:

• Satellite and aerial imagery for land use and infrastructure

• IoT sensor data for real-time environmental monitoring

• User-contributed scans for pedestrian-level detail

• LiDAR and 3D scanning for digital twins

• Operational business data to tie spatial context to outcomes

Processing Techniques

To generate value from data, LGMs use:

• Deep learning (e.g., convolutional neural networks) for feature recognition

• Computer vision to identify objects, detect change, and classify environments

• Natural language processing to connect text-based spatial references (e.g., from social media or field reports)

• Spatial statistics for predictive modeling and pattern detection

Resolution and Accuracy

• Spatial resolution: Determines granularity of insights

• Temporal resolution: Indicates update frequency

• Model confidence scoring: Provides transparency in predictions

• Ground-truth validation: Ensures real-world accuracy

Key Capabilities and Differentiators

Multi-Modal Data Fusion

LGMs synthesize multiple data streams—behavioral, environmental, structural—into a unified model. This fusion unlocks hidden insights and supports complex decision-making.

Temporal Modeling and Change Detection

By establishing historical baselines and identifying deviations, LGMs empower proactive interventions in urban systems, maintenance workflows, and risk management.

3D Reconstruction and Semantic Understanding

Niantic Spatial’s neural networks create semantically rich digital twins. VPS-based models understand both geometry and meaning of spaces, allowing for virtual inspections, pre-construction simulations, and immersive design scenarios.

Predictive Spatial Analytics

LGMs forecast demand, infrastructure stress, environmental impact, and crowd behavior. This lets businesses allocate resources more effectively and anticipate disruption before it occurs.

Spatial Relationship Modeling

By analyzing spatial dependencies and network effects, LGMs help enterprises optimize supply chains, reduce costs, and improve systemic resilience.

Enterprise Applications and Use Cases

These real-world implementations demonstrate how spatial intelligence transforms from theoretical capability to measurable business impact across industries.

Logistics and Supply Chain Optimization

By integrating real-time location data with predictive analytics, companies become more efficient in their logistics operations. For example, a global shipping company may implement a LGM to optimize its fleet routing, incorporating weather patterns, port congestion data, fuel consumption metrics, and delivery priorities.

Beyond transportation, geospatial models optimize warehouse placement and design. A consumer goods manufacturer could use spatial analysis to reconfigure its distribution network and reduce total facility square footage while cutting average delivery distance. The model would identify optimal locations by analyzing customer density, transportation infrastructure, labor availability, and property costs across thousands of potential sites.

Urban Planning and Infrastructure Management

Cities and utilities are leveraging spatial intelligence to create more resilient, efficient systems that better serve community needs while reducing costs. A regional utility could deploy a predictive maintenance system using geospatial modeling to identify potential infrastructure failures before they occurred. By analyzing soil conditions, weather patterns, infrastructure age, usage data, and maintenance history, the system would predict failure points, thereby reducing emergency repairs and extending average asset lifespan.

Retail and Location-Based Services

Retailers have embraced geospatial modeling to enhance site selection, optimize store networks, and deliver more personalized customer experiences based on location intelligence.

A retail chain could use LGM to integrate foot traffic patterns, demographic data, purchasing behavior, and competitive positioning and reveal previously undetected patterns in how weather conditions influenced product category performance at specific locations. VPS enables in-store navigation and product location at a granular level not possible with GPS alone. Location-based service providers use similar capabilities to improve user experiences and engagement.

Real Estate and Property Development

Real estate companies use spatial intelligence to evaluate, develop, and manage real estate assets. This reduces investment risk while identifying opportunities that traditional analysis might miss.

Picture a commercial real estate investment firm selecting sites. They implemented a geospatial model that combined traditional market data with emerging indicators like mobile device activity, transportation pattern changes, and business formation rates to identify high-potential locations months before conventional market indicators showed opportunity.

In property development, large geospatial models enable more accurate feasibility studies and impact assessments. A development company might create digital twins of proposed projects to simulate environmental impacts, traffic patterns, utility requirements, and economic effects before breaking ground. This could reduce planning costs while improving stakeholder engagement through immersive development scenarios.

Agriculture and Natural Resource Management

Precision agriculture and resource management delivers both economic and environmental benefits. An agricultural cooperative could implement a field-level optimization system that combines satellite imagery, soil sensors, weather data, and crop performance history. This might result in less water usage and continuous recommendations based on real-time conditions. Natural resource companies use similar approaches to improve exploration and extraction efficiency.

Getting Started with Large Geospatial Models

Success with LGMs starts with a solid data foundation, clear goals, and executive sponsorship.

Steps to implementation:

1. Pilot a specific use case with measurable ROI

2. Use existing data before pursuing new collection

3. Adopt modular platforms that support scale

4. Establish feedback loops between technical and business teams

Common Challenges and How to Overcome Them

• Data complexity: Use middleware to normalize formats

• Talent gaps: Partner with spatial AI experts or upskill internal teams

• User resistance: Integrate geospatial insights into familiar tools

• Scope creep: Develop phased roadmaps with clear value checkpoints

Why Niantic Spatial?

Niantic Spatial has trained over 50 million neural networks comprising more than 150 trillion parameters. These networks build detailed, learnable maps across millions of locations, enabling real-world understanding that is contextual, robust, and adaptive.

The Large Geospatial Model represents a shift from fragmented local models to a unified, foundational AI model of the physical world—one that can extrapolate beyond the data it’s seen by drawing from distributed, global spatial intelligence.

By distilling shared knowledge—like how churches or town squares typically look—Niantic Spatial’s LGM can infer missing scene data and improve localization from unfamiliar angles. This human-like understanding of space will power future AR, robotics, urban design, and more.

Looking Ahead: LGMs as Foundational AI

Just as Large Language Models (LLMs) changed how machines understand text, LGMs will revolutionize how machines comprehend and navigate the real world. These geospatial foundation models will interact with LLMs and multimodal systems to deliver immersive, intelligent spatial computing experiences.

In the near future, this will manifest in:

• AR glasses that deliver real-time, scene-aware insights

• Autonomous systems that navigate complex environments

• Location-aware assistants that blend digital content with the real world

Niantic Spatial is leading this evolution—building the world’s most advanced spatial intelligence platform from the ground up.