2026-07-21T11:24:03Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23841 2025-11-12T13:20:25Z ddc:004 open_access

Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing Wijegunarathna, Kalana Stock, Kristin Jones, Christopher B. Large Multi-Modal Models Large Language Models LLM Georeferencing Natural History collections 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 multi-modal 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Kalana Wijegunarathna and Kristin Stock and Christopher B. Jones 2025 Is Part Of LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2025.12 urn:nbn:de:0030-drops-238412 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.12 eng https://creativecommons.org/licenses/by/4.0/legalcode