Georeferencing Non-Gazetteered Place Names using Biological Specimen Records

2026-08-07

AI Summary

This study addresses the challenge of geolocating non-standard geographic place names (NGPs) in historical biological specimen records that are absent from modern gazetteers. The authors propose a novel text-based spatial reasoning framework that systematically integrates recurrent NGPs and their associated spatial relationship descriptions from specimen metadata. They evaluate three distinct approaches—deterministic modeling, probabilistic inference, and large language models (LLMs)—on a benchmark dataset of pseudo-NGPs. Experimental results demonstrate that probabilistic inference achieves the highest accuracy, yielding a median localization error of 1.43 kilometers and a 36% success rate within 1 kilometer, outperforming LLMs, which attain a median error of 1.80 kilometers and 31% 1-kilometer accuracy. This work establishes an effective new paradigm for precise georeferencing of historical biogeographic data.

Abstract

Biological specimen records collected by natural history institutions constitute a rich source of temporal geographic knowledge, capturing biodiversity information about regional landscapes as they were recorded at different times. Using digitised data from the Allan Herbarium (New Zealand), this study identifies place names in these specimen locality descriptions that are absent from current gazetteers; we refer to these as non-gazetteer place names (NGPs). These place names are typically historical, vernacular, or colloquial and were used as landmarks to describe a specimen's location at the time of collection. We then investigate the problem of georeferencing the NGPs using only the limited information available in the specimen records. To resolve this, we leverage repeated occurrences of the same place name across specimen records with different specimen locations and spatial relation terms, extracting and inverting these relations to derive constraints on NGP locations. This approach is instantiated within deterministic, probabilistic, and LLM-based methods, enabling a comparative analysis of their strengths and limitations for text-based spatial inference. On a pseudo-NGP benchmark, probabilistic inference achieves the highest accuracy (median error 1.43 km; A@1 km 36%), while the LLM yields competitive but less precise estimates (median error 1.80 km; A@1 km 31%), indicating that, despite advances in LLMs, traditional modelling remains advantageous when high spatial precision is required.

Problem

• georeferencing
• non-gazetteered place names
• biological specimen records
• spatial inference
• historical place names

Innovation

• non-gazetteered place names
• georeferencing
• spatial relation inference
• probabilistic modeling
• biological specimen records