GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
Rodrigo Ferreira Rodrigues, Karim Radouane, Jose G Moreno, Lynda Tamine
Abstract
In the context of geodata, existing Large Language Models have often been studied in a homogeneous setting, which has considerably limited insights into their generalization capabilities. In this paper, we present \benchName, a comprehensive benchmark for probing LLMs on geo-related tasks. We leverage a careful selection of twelve publicly available datasets from diverse geo-related tasks and domains, and evaluate a set of LLMs on geo-spatial and temporal understanding using our benchmark. Our results show that reasoning and size have a strong impact on overall performance. GeoBenchLLM is publicly available at https://github.com/Rfr2003/GeoBenchLLM.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.