Geospatial reasoning and awareness in large language models: a systematic review

Abstract

This research examines the evolution and integration of large language models within the geospatial domain, exploring both theoretical aspects and practical applications. Using a systematic review guided by the PRISMA methodology, the study investigates GeoAI developments and assesses the impact of foundation models in geospatial contexts. The findings highlight that commercial models demonstrate notable capabilities in interpreting geospatial concepts and generating functional code, although they face limitations concerning accessibility, transparency and reliance on external infrastructures. Smaller, open-source models, adapted through approaches such as fine-tuning and Retrieval-Augmented Generation, are identified as feasible alternatives, providing a balanced solution in terms of accuracy, efficiency and customization. The study emphasizes a need for large-scale, standardized datasets for effective training and evaluation of geospatial models, pointing toward a clear direction for future research. Despite significant advancements, achieving full autonomy of geospatial agents in complex task-solving scenarios remains an unresolved challenge. The future progression of GeoAI will rely heavily on interdisciplinary collaboration and the development of robust, transparent and ethical models capable of supporting real-time decision-making and promoting digital transformation in public administration and related fields.