IMAIA: Interactive Maps AI Assistant for Travel Planning and Geo-Spatial Intelligence

TL;DR

IMAIA is an interactive AI assistant that enables natural language queries and camera-based understanding of maps and geospatial scenes, improving user interaction and comprehension in map applications.

Contribution

It introduces a novel multi-component system that unifies language, maps, and geospatial data for enhanced, conversational map-based interactions and scene understanding.

Findings

Improves accuracy in map-centric question answering

Enhances camera-to-place grounding performance

Maintains low latency for user-facing deployment

Abstract

Map applications are still largely point-and-click, making it difficult to ask map-centric questions or connect what a camera sees to the surrounding geospatial context with view-conditioned inputs. We introduce IMAIA, an interactive Maps AI Assistant that enables natural-language interaction with both vector (street) maps and satellite imagery, and augments camera inputs with geospatial intelligence to help users understand the world. IMAIA comprises two complementary components. Maps Plus treats the map as first-class context by parsing tiled vector/satellite views into a grid-aligned representation that a language model can query to resolve deictic references (e.g., ``the flower-shaped building next to the park in the top-right''). Places AI Smart Assistant (PAISA) performs camera-aware place understanding by fusing image--place embeddings with geospatial signals (location, heading, proximity) to ground a scene, surface salient attributes, and generate concise explanations. A lightweight multi-agent design keeps latency low and exposes interpretable intermediate decisions. Across map-centric QA and camera-to-place grounding tasks, IMAIA improves accuracy and responsiveness over strong baselines while remaining practical for user-facing deployments. By unifying language, maps, and geospatial cues, IMAIA moves beyond scripted tools toward conversational mapping that is both spatially grounded and broadly usable.