Charting New Territories: Exploring the Geographic and Geospatial
Capabilities of Multimodal LLMs
- URL: http://arxiv.org/abs/2311.14656v3
- Date: Tue, 16 Jan 2024 18:20:08 GMT
- Title: Charting New Territories: Exploring the Geographic and Geospatial
Capabilities of Multimodal LLMs
- Authors: Jonathan Roberts, Timo L\"uddecke, Rehan Sheikh, Kai Han, Samuel
Albanie
- Abstract summary: Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored.
We conduct a series of experiments exploring various vision capabilities of MLLMs within these domains, particularly focusing on the frontier model GPT-4V.
Our methodology involves challenging these models with a small-scale geographic benchmark consisting of a suite of visual tasks, testing their abilities across a spectrum of complexity.
- Score: 35.86744469804952
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multimodal large language models (MLLMs) have shown remarkable capabilities
across a broad range of tasks but their knowledge and abilities in the
geographic and geospatial domains are yet to be explored, despite potential
wide-ranging benefits to navigation, environmental research, urban development,
and disaster response. We conduct a series of experiments exploring various
vision capabilities of MLLMs within these domains, particularly focusing on the
frontier model GPT-4V, and benchmark its performance against open-source
counterparts. Our methodology involves challenging these models with a
small-scale geographic benchmark consisting of a suite of visual tasks, testing
their abilities across a spectrum of complexity. The analysis uncovers not only
where such models excel, including instances where they outperform humans, but
also where they falter, providing a balanced view of their capabilities in the
geographic domain. To enable the comparison and evaluation of future models,
our benchmark will be publicly released.
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