# Georeferencing Non-Gazetteered Place Names using Biological Specimen Records

arXiv: https://arxiv.org/abs/2608.06884 · HTML: https://mlsanity.com/paper/2608.06884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding](https://mlsanity.com/paper/2408.11366/) (2024)
- [A Coherent Unsupervised Model for Toponym Resolution](https://mlsanity.com/paper/1805.01952/) (2018)
- [Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform](https://mlsanity.com/paper/2007.07455/) (2020)
- [TopoBERT: Plug and Play Toponym Recognition Module Harnessing Fine-tuned BERT](https://mlsanity.com/paper/2301.13631/) (2023)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)

## Abstract (arXiv)

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.