Abstract
Sustainability professionals, urban planners, clean energy developers, and other professionals encounter substantial challenges in efficiently analyzing and utilizing geospatial data for decision-making related to location opportunities, accessibility, and asset placement. Traditional approaches involve complex, multi-step workflows that require specialized expertise and time-consuming analysis across disparate data sources, resulting in substantial delays before actionable insights can be derived. This disclosure describes techniques for automating geospatial workflows over multiple datasets from varied sources, facilitated by large language model (LLM) agents. The techniques enable users to efficiently query geospatial data and receive insights through an AI-driven process that minimizes complexity and reduces time delays.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Blohmé, Patrik; Gupta, Ananya; Murphy, Gearoid; Ponda, Sameera; Merrikh-Yazdi, Danusch; Jung, Gia; Johnston, Renee; and Sieniek, Marcin, "Automating Geospatial Analysis Using Large Language Models and Code Execution in a Persistent Environment", Technical Disclosure Commons, (July 30, 2025)
https://www.tdcommons.org/dpubs_series/8407