Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencing
This addresses the labour-intensive task of georeferencing for collection agencies, though it is incremental as it builds on existing LMM capabilities.
The paper tackles the problem of georeferencing un-georeferenced biological sample records by developing a novel method that uses Large Multi-Modal Models (LMMs) to visually contextualize spatial relations from locality descriptions with maps, achieving an average distance error of approximately 1 km in preliminary experiments.
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 ($\sim$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.