11 citations as recorded by crossref.

1. On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper) G. Mai et al. https://doi.org/10.1145/3653070
2. Toponym resolution leveraging lightweight and open-source large language models and geo-knowledge X. Hu et al. https://doi.org/10.1080/13658816.2024.2405182
3. Georeferencing complex relative locality descriptions with large language models A. Fernando et al. https://doi.org/10.1080/13658816.2026.2613291
4. The generative revolution: AI foundation models in geospatial health—applications, challenges and future research B. Resch et al. https://doi.org/10.1186/s12942-025-00391-0
5. Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning G. Mai et al. https://doi.org/10.1111/tgis.13012
6. TopoBERT: a plug and play toponym recognition module harnessing fine-tuned BERT B. Zhou et al. https://doi.org/10.1080/17538947.2023.2239794
7. Analyzing Geographic Bias of Newspaper Articles Reporting Global Climate Disasters I. Kong & R. Purves https://doi.org/10.1080/24694452.2025.2564220
8. Evaluation of geographical distortions in language models R. Decoupes et al. https://doi.org/10.1007/s10994-025-06916-9
9. Geographical and linguistic perspectives on developing geoparsers with generic resources T. Leppämäki et al. https://doi.org/10.1080/13658816.2024.2369539
10. Urban Computing in the Era of Large Language Models Z. Li et al. https://doi.org/10.1145/3768163
11. More than just Tweets: the potential of alternative geo-social media data for disaster management N. Vračević et al. https://doi.org/10.1007/s13278-025-01494-z