Wijegunarathna, Kalana, Stock, Kristin and Jones, Christopher B  ORCID: https://orcid.org/0000-0001-6847-7575
2025.
Large multi-modal model cartographic map comprehension for textual locality georeferencing.
Presented at: 13th International Conference on Geographic Information Science (GIScience 2025),
Christchurch, New Zealand,
26-29 August 2025.
Leibniz International Proceedings in Informatics (LIPIcs).
, vol.346
Leibniz:
Schloss Dagstuhl,
pp. 1-19.
10.4230/LIPIcs.GIScience.2025.12
|
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
Millions of biological sample records collected in the last few centuries archived in natural history collections are un-georeferenced. Georeferencing complex locality descriptions associated with these collection samples is a highly labour-intensive task collection agencies struggle with. None of the existing automated methods exploit maps that are an essential tool for georeferencing complex relations. We present preliminary experiments and results of a novel method that exploits multimodal capabilities of recent Large Multi-Modal Models (LMM). This method enables the model to visually contextualize spatial relations it reads in the locality description. We use a grid-based approach to adapt these auto-regressive models for this task in a zero-shot setting. Our experiments conducted on a small manually annotated dataset show impressive results for our approach (∼1 km average distance error) compared to uni-modal georeferencing with Large Language Models and existing georeferencing tools. The paper also discusses the findings of the experiments in light of an LMM’s ability to comprehend fine-grained maps. Motivated by these results, a practical framework is proposed to integrate this method into a georeferencing workflow.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Schloss Dagstuhl |
| ISBN: | 978-3-95977-378-2 |
| Funders: | Ministry of Business Innovation and Employment Smart Ideas Fund (grant number MAUX2104) |
| Related URLs: | |
| Date of First Compliant Deposit: | 23 October 2025 |
| Date of Acceptance: | 8 April 2025 |
| Last Modified: | 24 Oct 2025 15:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181854 |
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric