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Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs

Jonathan Roberts, Timo Lüddecke, Rehan Sheikh, Kai Han, Samuel Albanie

TL;DR

This work evaluates the geographic and geospatial reasoning capabilities of multimodal LLMs, focusing on GPT-4V and several open-source baselines, through a curated, small-scale benchmark. It combines localisation, remote sensing interpretation, mapping, and flag-identification tasks across natural, abstract, and RS imagery to map strengths, weaknesses, and biases. Key findings show GPT-4V attains broad task coverage and strong sentence-level reasoning but struggles with precise localization and object-level delineation, while open-source models often excel at localization and certain RS tasks. The authors release their benchmark to enable reproducibility and cross-model comparisons, highlighting practical implications for navigation, environmental monitoring, disaster response, and awareness of regional biases in training data.

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.

Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs

TL;DR

This work evaluates the geographic and geospatial reasoning capabilities of multimodal LLMs, focusing on GPT-4V and several open-source baselines, through a curated, small-scale benchmark. It combines localisation, remote sensing interpretation, mapping, and flag-identification tasks across natural, abstract, and RS imagery to map strengths, weaknesses, and biases. Key findings show GPT-4V attains broad task coverage and strong sentence-level reasoning but struggles with precise localization and object-level delineation, while open-source models often excel at localization and certain RS tasks. The authors release their benchmark to enable reproducibility and cross-model comparisons, highlighting practical implications for navigation, environmental monitoring, disaster response, and awareness of regional biases in training data.

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.
Paper Structure (50 sections, 15 figures, 2 tables)

This paper contains 50 sections, 15 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Experimental Design
  5. Models
  6. Prompting
  7. Experiments
  8. Localisation
  9. Remote Sensing
  10. Classification.
  11. Change Detection.
  12. Segmentation.
  13. Bounding Boxes.
  14. Counting.
  15. Mapping
  16. ...and 35 more sections

Figures (15)

  • Figure 1: Satellite imagery change detection. We test GPT-4V's ability to detect seasonal changes in a four-image time-series from wang2023ssl4eos12. In this example, the model is able to pick up minor details such as crop colouration and the presence of snow to correctly estimate seasons.
  • Figure 2: Segmentation using GPT-4V. We include examples of Grid (c) and SVG (d) segmentation, and localisation (e) of satellite imagery (a) from LoveDA wang2022loveda. Bounding boxes are for urban areas and road. Segmentation labels are given in (f).
  • Figure 3: Bounding boxes for urban areas, road and water bodies.
  • Figure 4: Counting small objects proves challenging.
  • Figure 5: Identification: [L]: quantitative results for all three identification tasks. [R]: selected examples for each task indicating success and failure of the individual models (map data from OpenStreetMap). (Note, high resolution images were provided to the models.)
  • ...and 10 more figures