Abstract
Scenario Generation Language (SGL) is a powerful tool that simplifies geospatial analysis and decision-making processes, removing the requirement for users to have expertise in GIS or SQL. However, users still need to understand the SGL grammar. This paper introduces a novel approach that utilizes GPT (Generative Pre-trained Transformer) - LLM (Large Language Model) to generate SGL statements directly from natural language questions. By leveraging the capabilities of GPT-LLM, this approach bridges the gap between user intent and technical query construction, enhancing the usability and accessibility of SGL. It enables decision-makers to interact with geospatial data using familiar natural language queries, without the need for in-depth knowledge of SGL or complex geospatial querying techniques. The integration of natural language processing with SGL empowers users to effortlessly generate accurate and syntactically correct statements, streamlining the analysis process and facilitating scenario exploration. Experimental results indicate that directly utilizing GPT-LLM for geospatial analysis may not yield satisfactory results. However, the approach presented in this paper demonstrates its effectiveness in simplifying geospatial analysis and supporting informed decision-making.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, M., et al.: Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41, 1–8 (2023)
Imani, S., Du, L., Shrivastava, H.: MathPrompter: mathematical reasoning using large language models. arXiv preprint arXiv:2303.05398 (2023)
Shinn, N., Cassano, F., Labash, B., Gopinath, A., Narasimhan, K., Yao, S.: Reflexion: language agents with verbal reinforcement learning. [arXiv preprint]. arXiv:2303.11366 (2023)
Frez, J., Baloian, N., Zurita, G., Pino, J.A.: Dealing with incomplete and uncertain context data in geographic information systems. In: Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 129–134. IEEE explore, Hsinchu, Taiwan, May 21–23 (2014)
Frez, J., Baloian, N., Zurita, G.: Getting serious about integrating decision support mechanisms into geographic information systems. In: Ninth International Conference on Computer Science and Information Technologies Revised Selected Papers, pp. 1–11. IEEE explore, Yerevan, Armenia (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Frez, J., Baloian, N. (2023). Bridging the Gap: Enhancing Geospatial Analysis with Natural Language and Scenario Generation Language. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-48642-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48641-8
Online ISBN: 978-3-031-48642-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)