LLM-Enhanced Processing of Complex Spatial Queries

Abstract

Traditional spatial databases and map services handle deterministic queries well, but struggle to handle intent-rich and complex spatial queries. Even a simple request such as “In Melbourne, find a convenient petrol stop along the Tullamarine Freeway to the airport, avoiding tolls and minimizing detour” often breaks current systems, revealing a gap between natural-language intent and executable spatial operations. To fill this gap, we introduce an LLM-driven framework that parses user intent, decomposes complex spatial requests into executable sub-queries, schedules the necessary Google Maps API calls, and synthesizes the final answer. To enable systematic study, we curate a dataset of compl..