Geographic Inference with Large Language Models: Identification of POIs and Urban Features from Addresses and Coordinates - Silva, Gabriel A. C.; Silva, Salatiel D.; Campelo, Claudio E. C. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2026.249658, 2026)
This paper evaluates the ability of Large Language Models (LLMs) to infer geographic features and identify points of interest (POIs) from addresses and coordinates. Nine cities of different urban sizes, distributed across three continents, were analyzed, with five addresses per city in scenarios such as urban center, residential area, green area, proximity to water and mixed area. The predictions were validated with external geospatial databases. The results indicate greater stability in feature inference than in POI identification, whose performance varies according to country and urban size; Gemini 3.1 Flash Lite leads in large cities, GPT-4.1 leads in medium-sized cities, and Gemini 3 Flash Preview leads in small cities.