Understand

Geo-referenced spatial intelligence at the pixel level, combining segmentation and contextual understanding to give humans, robots, and language models and AI agents real-time awareness for smarter and more efficient decision-making.

Built on open-set semantics, Niantic Spatial Understand encodes each 3D point with meaning, enabling context-rich, queryable 3D maps interpretable by humans and machines.

Semantic Understanding

Key Capabilities

Per-point semantic understanding: Each spatial point encodes an open, high-dimensional descriptor capturing geometry, material, and contextual features.

Open-set generalization: Identifies and adapts to new concepts without retraining or relying on fixed category taxonomies.

Cross-modal grounding: Connects visual, spatial, and linguistic information within a unified semantic framework.

Continuous semantics: Delivers nuanced understanding of relationships across objects, surfaces, and environments for richer spatial reasoning.

Niantic Spatial Understand in Action

What Understand Enables

Queryable maps: Ask open-ended spatial questions like “Where does vegetation encroach on power lines or access roads?”, “What terrain is traversable for inspection vehicles or drones?”, and “Which structures provide shelter or shade for field operations?”

Context-aware autonomy: Robots and AI agents reason about their surroundings using meaning, not just geometry.

Adaptive intelligence: Maps evolve with new data, improving recognition and inference over time.

Built for integration: Designed for multi-modal queries across enterprise systems, Physical AI agents, and in-field robotics workflows.

Built for the Real World

Public Sector

Real-time scene interpretation, situational awareness, and mission-level reasoning for safer, more informed operations.

Robotics & Autonomy

Semantic navigation, task recognition, and adaptive decision-making powered by spatial understanding.

Intelligent Field Operations

Automates inspection and asset intelligence for oil and gas, utilities, and large-scale infrastructure environments.